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Background: Although it is well known that renal artery stenosis may cause renovascular hypertension, it is unclear how the degree of stenosis should best be measured in morphological images. The aim of this study was to determine which morphological measures from Computed Tomography Angiography (CTA) and Magnetic Resonance Angiography (MRA) are best in predicting whether a renal artery stenosis is hemodynamically significant or not. Methods: Forty-seven patients with hypertension and a clinical suspicion of renovascular hypertension were examined with CTA, MRA, captopril-enhanced renography (CER) and captopril test (Ctest). CTA and MRA images of the renal arteries were analyzed by two readers using interactive vessel segmentation software. The measures included minimum diameter, minimum area, diameter reduction and area reduction. In addition, two radiologists visually judged the diameter reduction without automated segmentation. The results were then compared using limits of agreement and intra-class correlation, and correlated with the results from CER combined with Ctest (which were used as standard of reference) using receiver operating characteristics (ROC) analysis. Results: A total of 68 kidneys had all three investigations (CTA, MRA and CER + Ctest), where 11 kidneys (16.2 %) got a positive result on the CER + Ctest. The greatest area under ROC curve (AUROC) was found for the area reduction on MRA, with a value of 0.91 (95 % confidence interval 0.82-0.99), excluding accessory renal arteries. As comparison, the AUROC for the radiologists' visual assessments on CTA and MRA were 0.90 (0.82-0.98) and 0.91 (0.83-0.99) respectively. None of the differences were statistically significant. Conclusions: No significant differences were found between the morphological measures in their ability to predict hemodynamically significant stenosis, but a tendency of MRA having higher AUROC than CTA. There was no significant difference between measurements made by the radiologists and measurements made with fuzzy connectedness segmentation. Further studies are required to definitely identify the optimal measurement approach.
One of the major differences between medical doctor training and machine learning is that doctors are trained to recognize normal/healthy anatomy first. Knowing the healthy appearance of anatomy structures helps doctors to make better judgement when some abnormality shows up in an image. In this study, we propose a normal appearance autoencoder (NAA), that removes abnormalities from a diseased image. This autoencoder is semi-automatically trained using another partial convolutional in-paint network that is trained using healthy subjects only. The output of the autoencoder is then fed to a segmentation net in addition to the original input image, i.e. the latter gets both the diseased image and a simulated healthy image where the lesion is artificially removed. By getting access to knowledge of how the abnormal region is supposed to look, we hypothesized that the segmentation network could perform better than just being shown the original slice. We tested the proposed network on the LIDC-IDRI dataset for lung cancer detection and segmentation. The preliminary results show the NAA approach improved segmentation accuracy substantially in comparison with the conventional U-Net architecture.
Purpose: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy. Methods: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC). Results: The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROC(sALop) = 0.90 vs. AUROC(radiomic) = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values. Conclusion: A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.
In MRI neuroimaging, the shimming procedure is used before image acquisition to correct for inhomogeneity of the static magnetic field within the brain. To correctly adjust the field, the brain's location and edges must first be identified from quickly-acquired low resolution data. This process is currently carried out manually by an operator, which can be time-consuming and not always accurate. In this work, we implement a quick and automatic technique for brain segmentation to be potentially used during the shimming. Our method is based on two main steps. First, a random forest classifier is used to get a preliminary segmentation from an input MRI image. Subsequently, a statistical shape model of the brain, which was previously generated from ground-truth segmentations, is fitted to the output of the classifier to obtain a model-based segmentation mask. In this way, a-priori knowledge on the brain's shape is included in the segmentation pipeline. The proposed methodology was tested on low resolution images of rat brains and further validated on rabbit brain images of higher resolution. Our results suggest that the present method is promising for the desired purpose in terms of time efficiency, segmentation accuracy and repeatability. Moreover, the use of shape modeling was shown to be particularly useful when handling low-resolution data, which could lead to erroneous classifications when using only machine learning-based methods.
Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from 3 experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.
The most characteristic feature of domestic animals is their change in behavior associated with selection for tameness. Here we show, using high-resolution brain magnetic resonance imaging in wild and domestic rabbits, that domestication reduced amygdala volume and enlarged medial prefrontal cortex volume, supporting that areas driving fear have lost volume while areas modulating negative affect have gained volume during domestication. In contrast to the localized gray matter alterations, white matter anisotropy was reduced in the corona radiata, corpus callosum, and the subcortical white matter. This suggests a compromised white matter structural integrity in projection and association fibers affecting both afferent and efferent neural flow, consistent with reduced neural processing. We propose that compared with their wild ancestors, domestic rabbits are less fearful and have an attenuated flight response because of these changes in brain architecture.
Purpose: A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested. Methods: The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border. Mean intensity changes are computed within each zone, for CT and PET scans separately, and used as image features for tumor response assessment. Doing so, tumors are described by accounting for temporal and spatial changes at the same time. Using linear support vector machines, the new features were tested on 30 non-small cell lung cancer patients who underwent sequential or concurrent chemoradiotherapy. Prediction of 2-years overall survival was based on two PET-CT scans, acquired before the start and during the first 3 weeks of treatment. The predictive power of the newly proposed longitudinal pattern features was compared to that of previously proposed radiomics features and radiobiological parameters. Results: The highest areas under the receiver operating characteristic curves were 0.98 and 0.93 for patients treated with sequential and concurrent chemoradiotherapy, respectively. Results showed an overall comparable performance with respect to radiomics features and radiobiological parameters. Conclusions: A novel set of quantitative image features, based on underlying tumor physiology, was computed from PET/CT scans and successfully employed to distinguish between early responders and non-responders to chemoradiotherapy.
Cortical bone plays a big role in the mechanical competence of bone. The analysis of cortical bone requires accurate segmentation methods. Level set methods are usually in the state-of-the-art for segmenting medical images. However, traditional implementations of this method are computationally expensive. This drawback was recently tackled through the so-called coherent propagation extension of the classical algorithm which has decreased computation times dramatically. In this study, we assess the potential of this technique for segmenting cortical bone in interactive time in 3D images acquired through High Resolution peripheral Quantitative Computed Tomography (HR-pQCT). The obtained segmentations are used to estimate cortical thickness and cortical porosity of the investigated images. Cortical thickness and Cortical porosity is computed using sphere fitting and mathematical morphological operations respectively. Qualitative comparison between the segmentations of our proposed algorithm and a previously published approach on six images volumes reveals superior smoothness properties of the level set approach. While the proposed method yields similar results to previous approaches in regions where the boundary between trabecular and cortical bone is well defined, it yields more stable segmentations in challenging regions. This results in more stable estimation of parameters of cortical bone. The proposed technique takes few seconds to compute, which makes it suitable for clinical settings.
We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning,.), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.
Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CIA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CIA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (I) (semi-)automatically detect and quantify stenosis on CIA, in comparison with quantitative coronary angiography (QCA) and CIA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CIA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CIA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/. (C) 2013 Elsevier B.V. All rights reserved.
Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task.In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels - nodes - that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%.
This chapter focuses on skeleton detection for clinical evaluation of blood vessel systems. In clinical evaluation, there is a need for fast and accurate segmentation algorithms that can reliably provide vessel measurements and additional information for clinicians to decide the diagnosis.Since blood vessels have a characteristic tubular shape, their segmentation can be accelerated and facilitated by first identifying the rough vessel centerlines, which can be seen as a special case of an image skeleton extraction algorithm. A segmentation algorithm will finally use the resulting skeleton as a seed region during the segmentation. The proposed method takes an unprocessed 3D computed tomography angiography (CTA) scan as an input and generates a connected graph of centrally located arterial voxels. The method works in two levels, where large arteries are captured in the first level, and small arteries are added in the second one. Experimental results show that the method can achieve high overlap rate and acceptable detection rate accuracies. High computational efficiency of the method opens the possibility for an interactive clinical use.
We present a coverage segmentation method for extracting thin structures in three-dimensional images. The proposed method is an improved extension of our coverage segmentation method for 2D thin structures. We suggest implementation that enables low memory consumption and processing time, and by that applicability of the method on real CTA data. The method needs a reliable crisp segmentation as an input and uses information from linear unmixing and the crisp segmentation to create a high-resolution crisp reconstruction of the object, which can then be used as a final result, or down-sampled to a coverage segmentation at the starting image resolution. Performed quantitative and qualitative analysis confirm excellent performance of the proposed method, both on synthetic and on real data, in particular in terms of robustness to noise.
Breast cancer is the most common cancer type in women worldwide. Histological evaluation of the breast biopsies is a challenging task even for experienced pathologists. In this paper, we propose a fully automatic method to classify breast cancer histological images to four classes, namely normal, benign, in situ carcinoma and invasive carcinoma. The proposed method takes normalized hematoxylin and eosin stained images as input and gives the final prediction by fusing the output of two residual neural networks (ResNet) of different depth. These ResNets were first pre-trained on ImageNet images, and then fine-tuned on breast histological images. We found that our approach outperformed a previous published method by a large margin when applied on the BioImaging 2015 challenge dataset yielding an accuracy of 97.22%. Moreover, the same approach provided an excellent classification performance with an accuracy of 88.50% when applied on the ICIAR 2018 grand challenge dataset using 5-fold cross validation.
Malignant melanoma is one of the most aggressive forms of skin cancer. Early detection is important as it significantly improves survival rates. Consequently, accurate discrimination of malignant skin lesions from benign lesions such as seborrheic keratoses or benign nevi is crucial, while accurate computerised classification of skin lesion images is of great interest to support diagnosis. In this paper, we propose a fully automatic computerised method to classify skin lesions from dermoscopic images. Our approach is based on a novel ensemble scheme for convolutional neural networks (CNNs) that combines intra-architecture and inter-architecture network fusion. The proposed method consists of multiple sets of CNNs of different architecture that represent different feature abstraction levels. Each set of CNNs consists of a number of pre-trained networks that have identical architecture but are fine-tuned on dermoscopic skin lesion images with different settings. The deep features of each network were used to train different support vector machine classifiers. Finally, the average prediction probability classification vectors from different sets are fused to provide the final prediction. Evaluated on the 600 test images of the ISIC 2017 skin lesion classification challenge, the proposed algorithm yields an area under receiver operating characteristic curve of 87.3% for melanoma classification and an area under receiver operating characteristic curve of 95.5% for seborrheic keratosis classification, outperforming the top-ranked methods of the challenge while being simpler compared to them. The obtained results convincingly demonstrate our proposed approach to represent a reliable and robust method for feature extraction, model fusion and classification of dermoscopic skin lesion images.
Nuclei segmentation is an important but challenging task in the analysis of hematoxylin and eosin (H&E)-stained tissue sections. While various segmentation methods have been proposed, machine learning-based algorithms and in particular deep learning-based models have been shown to deliver better segmentation performance. In this work, we propose a novel approach to segment touching nuclei in H&E-stained microscopic images using U-Net-based models in two sequential stages. In the first stage, we perform semantic segmentation using a classification U-Net that separates nuclei from the background. In the second stage, the distance map of each nucleus is created using a regression U-Net. The final instance segmentation masks are then created using a watershed algorithm based on the distance maps. Evaluated on a publicly available dataset containing images from various human organs, the proposed algorithm achieves an average aggregate Jaccard index of 56.87%, outperforming several state-of-the-art algorithms applied on the same dataset.
Segmenting brain tissue from MR scans is thought to be highly beneficial for brain abnormality diagnosis, prognosis monitoring, and treatment evaluation. Many automatic or semi-automatic methods have been proposed in the literature in order to reduce the requirement of user intervention, but the level of accuracy in most cases is still inferior to that of manual segmentation. We propose a new brain segmentation method that integrates volumetric shape models into a supervised artificial neural network (ANN) framework. This is done by running a preliminary level-set based statistical shape fitting process guided by the image intensity and then passing the signed distance maps of several key structures to the ANN as feature channels, in addition to the conventional spatial-based and intensity-based image features. The so-called shape context information is expected to help the ANN to learn local adaptive classification rules instead of applying universal rules directly on the local appearance features. The proposed method was tested on a public datasets available within the open MICCAI grand challenge (MRBrainS13). The obtained average Dice coefficient were 84.78%, 88.47%, 82.76%, 95.37% and 97.73% for gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), brain (WM + GM) and intracranial volume respectively. Compared with other methods tested on the same dataset, the proposed method achieved competitive results with comparatively shorter training time.
Skin cancer is one of the major types of cancers with an increasing incidence over the past decades. Accurately diagnosing skin lesions to discriminate between benign and malignant skin lesions is crucial to ensure appropriate patient treatment. While there are many computerised methods for skin lesion classification, convolutional neural networks (CNNs) have been shown to be superior over classical methods. In this work, we propose a fully automatic computerised method for skin lesion classification which employs optimised deep features from a number of well-established CNNs and from different abstraction levels. We use three pre-trained deep models, namely AlexNet, VGG16 and ResNet-18, as deep feature generators. The extracted features then are used to train support vector machine classifiers. In a final stage, the classifier outputs are fused to obtain a classification. Evaluated on the 150 validation images from the ISIC 2017 classification challenge, the proposed method is shown to achieve very good classification performance, yielding an area under receiver operating characteristic curve of 83.83% for melanoma classification and of 97.55% for seborrheic keratosis classification.
Multiple sclerosis (MS) is a demyelinating disease which could cause severe motor and cognitive deterioration. Segmenting MS lesions could be highly beneficial for diagnosing, analyzing and monitoring treatment efficacy. To do so, manual segmentation, performed by experts, is the conventional method in hospitals and clinical environments. Although manual segmentation is accurate, it is time consuming, expensive and might not be reliable. The aim of this work was to propose an automatic method for MS lesion segmentation and evaluate it using brain images available within the MICCAI MS segmentation challenge. The proposed method employs supervised artificial neural network based algorithm, exploiting intensity-based and spatial-based features as the input of the network. This method achieved relatively accurate results with acceptable training and testing time for training datasets.
We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformation methods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstruction using three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlines are manually selected in the images. Using the properties of the Hessian matrix, the centerline points are assigned direction information. For correspondence matching, a combination of methods was used. The process starts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labeling and an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline with decreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation in virtual, phantom, and experimental images, including intraoperative data from patient experiments, shows that, with appropriate camera positions, the error estimates (root-mean square error and mean error) are similar to 1 mm.
In this study we present a non-rigid point set registration for 3D curves (composed by 3D set of points). The method was evaluated in the task of registration of 3D superficial vessels of the brain where it was used to match vessel centerline points. It consists of a combination of the Coherent Point Drift (CPD) and the Thin-Plate Spline (TPS) semilandmarks. The CPD is used to perform the initial matching of centerline 3D points, while the semilandmark method iteratively relaxes/slides the points. For the evaluation, a Magnetic Resonance Angiography (MRA) dataset was used. Deformations were applied to the extracted vessels centerlines to simulate brain bulging and sinking, using a TPS deformation where a few control points were manipulated to obtain the desired transformation (T-1). Once the correspondences are known, the corresponding points are used to define a new TPS deformation(T-2). The errors are measured in the deformed space, by transforming the original points using T-1 and T-2 and measuring the distance between them. To simulate cases where the deformed vessel data is incomplete, parts of the reference vessels were cut and then deformed. Furthermore, anisotropic normally distributed noise was added. The results show that the error estimates (root mean square error and mean error) are below 1 mm, even in the presence of noise and incomplete data.
During percutaneous coronary intervention, stents are placed in narrowings of the arteries to restore normal blood flow. Despite improvements in stent design, deployment techniques and drug-eluting coatings, restenosis and stent thrombosis remain a significant problem. Population stent design based on statistical shape analysis may improve clinical outcomes. Computed tomographic (CT) coronary angiography scans from 211 patients with a zero calcium score, no stenoses and no intermediate artery, were used to create statistical shape models of 446 major coronary artery bifurcations (left main, first diagonal and obtuse marginal and right coronary crux). Coherent point drift was used for registration. Principal component analysis shape scores were tested against clinical risk factors, quantifying the importance of recognised shape features in intervention including size, angles and curvature. Significant differences were found in (1) vessel size and bifurcation angle between the left main and other bifurcations; (2) inlet and curvature angle between the right coronary crux and other bifurcations; and (3) size and bifurcation angle by sex. Hypertension, smoking history and diabetes did not appear to have an association with shape. Physiological diameter laws were compared, with the Huo-Kassab model having the best fit. Bifurcation coronary anatomy can be partitioned into clinically meaningful modes of variation showing significant shape differences. A computational atlas of normal coronary bifurcation shape, where disease is common, may aid in the design of new stents and deployment techniques, by providing data for bench-top testing and computational modelling of blood flow and vessel wall mechanics.
Describing the detailed statistical anatomy of the coronary artery tree is important for determining the ætiology of heart disease. A number of studies have investigated geometrical features and have found that these correlate with clinical outcomes, e.g. bifurcation angle with major adverse cardiac events. These methodologies were mainly two-dimensional, manual and prone to inter-observer variability, and the data commonly relates to cases already with pathology. We propose a hybrid atlasing methodology to build a population of computational models of the coronary arteries to comprehensively and accurately assess anatomy including 3D size, geometry and shape descriptors. A random sample of 122 cardiac CT scans with a calcium score of zero was segmented and analysed using a standardised protocol. The resulting atlas includes, but is not limited to, the distributions of the coronary tree in terms of angles, diameters, centrelines, principal component shape analysis and cross-sectional contours. This novel resource will facilitate the improvement of stent design and provide a reference for hemodynamic simulations, and provides a basis for large normal and pathological databases.
Aims: The aim of this study was to define the shape variations, including diameters and angles, of the major coronary artery bifurcations. Methods and results: Computed tomographic angiograms from 300 adults with a zero calcium score and no stenoses were segmented for centreline and luminal models. A computational atlas was constructed enabling automatic quantification of 3D angles, diameters and lengths of the coronary tree. The diameter (mean +/- SD) of the left main coronary was 3.5 +/- 0.8 mm and the length 10.5 +/- 5.3 mm. The left main bifurcation angle (distal angle or angle B) was 89 +/- 21 degrees for cases with, and 75 +/- 23 degrees for those without an intermediate artery (p<0.001). Analogous measurements of diameter and angle were tabulated for the other major bifurcations (left anterior descending/diagonal, circumflex/obtuse marginal and right coronary crux). Novel 3D angle definitions are proposed and analysed. Conclusions: A computational atlas of normal coronary artery anatomy provides distributions of diameter, lengths and bifurcation angles as well as more complex shape analysis. These data define normal anatomical variation, facilitating stent design, selection and optimal treatment strategy. These population models are necessary for accurate computational flow dynamics, can be 3D printed for bench testing bifurcation stents and deployment strategies, and can aid in the discussion of different approaches to the treatment of coronary bifurcations.
Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.
This paper proposes an automatic segmentation method of vessel walls that combines an implicit 3D model of the vessels and a total curvature penalizer in a level set evolution scheme. First, the lumen is segmented by alternating a model-guided level set evolution and a recalculation of the model itself. Second, the level set of the lumen is evolved with a term that aims at penalizing the total curvature and with a prior that forces the outer layer of the vessel towards the outside of the lumen. The model term is deactivated during this step. Finally, in a third step, the model term is reactivated in order to impose a smooth change of the radius along the vessel. Once the two segmentations have been computed, stenoses are detected and quantified at the thickest locations of the segmented vessel wall. Preliminary results show that the proposed method compares favorably with respect to the state-of-the-art both for synthetic and real CTA datasets.
Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substantial training and experience, and even then the rating agreement between two radiologists is not perfect. We have developed a model we call AVRA (Automatic Visual Ratings of Atrophy) based on machine learning methods and trained on 2350 visual ratings made by an experienced neuroradiologist. It provides fast and automatic ratings for Scheltens' scale of medial temporal atrophy (MTA), the frontal subscale of Pasquier's Global Cortical Atrophy (GCA-F) scale, and Koedam's scale of Posterior Atrophy (PA). We demonstrate substantial inter-rater agreement between AVRA's and a neuroradiologist ratings with Cohen's weighted kappa values of kappa(w) = 0.74/0.72 (MTA left/right), kappa(w) = 0.62 (GCA-F) and kappa(w) = 0.74 (PA). We conclude that automatic visual ratings of atrophy can potentially have great scientific value, and aim to present AVRA as a freely available toolbox.
Despite a long tradition, conventional anatomy education based on dissection is declining. This study tested a new virtual reality (VR) technique for anatomy learning based on virtual contrast injection. The aim was to assess whether students value this new three-dimensional (3D) visualization method as a learning tool and what value they gain from its use in reaching their anatomical learning objectives. Several 3D vascular VR models were created using an interactive segmentation tool based on the "virtual contrast injection" method. This method allows users, with relative ease, to convert computer tomography or magnetic resonance images into vivid 3D VR movies using the OsiriX software equipped with the CMIV CTA plug-in. Once created using the segmentation tool, the image series were exported in Quick Time Virtual Reality (QTVR) format and integrated within a web framework of the Educational Virtual Anatomy (EVA) program. A total of nine QTVR movies were produced encompassing most of the major arteries of the body. These movies were supplemented with associated information, color keys, and notes. The results indicate that, in general, students' attitudes towards the EVA-program were positive when compared with anatomy textbooks, but results were not the same with dissections. Additionally, knowledge tests suggest a potentially beneficial effect on learning.
PurposeSurgical navigation system (SNS) has been an important tool in surgery. However, the complicated and tedious manual selection of fiducial points on preoperative images for registration affects operational efficiency to large extent. In this study, an oral and maxillofacial navigation system named BeiDou-SNS with automatic identification of fiducial points was developed and demonstrated.MethodsTo solve the fiducial selection problem, a novel method of automatic localization for titanium screw markers in preoperative images is proposed on the basis of a sequence of two local mean-shift segmentation including removal of metal artifacts. The operation of the BeiDou-SNS consists of the following key steps: The selection of fiducial points, the calibration of surgical instruments, and the registration of patient space and image space. Eight cases of patients with titanium screws as fiducial markers were carried out to analyze the accuracy of the automatic fiducial point localization algorithm. Finally, a complete phantom experiment of zygomatic implant placement surgery was performed to evaluate the whole performance of BeiDou-SNS. Results and conclusionThe coverage of Euclidean distances between fiducial marker positions selected automatically and those selected manually by an experienced dentist for all eight cases ranged from 0.373 to 0.847mm. Four implants were inserted into the 3D-printed model under the guide of BeiDou-SNS. And the maximal deviations between the actual and planned implant were 1.328mm and 2.326mm, respectively, for the entry and end point while the angular deviation ranged from 1.094 degrees to 2.395 degrees. The results demonstrate that the oral surgical navigation system with automatic identification of fiducial points can meet the requirements of the clinical surgeries.
Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: (1) a method is described to create a consensus centerline with multiple observers, (2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, (3) a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available. and (4) 13 coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms.
Methods and Results-Thirty-six patients with normal coronary arteries determined by 320 x 0.5-mm detector row coronary CTA were retrospectively evaluated with customized image postprocessing software to measure Hounsfield Units at 1-mm intervals orthogonal to the artery center line. Linear regression determined correlation between mean Hounsfield Units and distance from the coronary ostium (regression slope defined as the distance gradient G(d)), lumen cross-sectional area (G(a)), and lumen short-axis diameter (G(s)). For each gradient, differences between the 3 coronary arteries were analyzed with ANOVA. Linear regression determined correlations between measured gradients, heart rate, body mass index, and cardiac phase. To determine feasibility in lesions, all 3 gradients were evaluated in 22 consecutive patients with left anterior descending artery lesions >= 50% stenosis. For all 3 coronary arteries in all patients, the gradients G(a) and G(s) were significantly different from zero (P < 0.0001), highly linear (Pearson r values, 0.77 to 0.84), and had no significant difference between the left anterior descending, left circumflex, and right coronary arteries (P > 0.503). The distance gradient G(d) demonstrated nonlinearities in a small number of vessels and was significantly smaller in the right coronary artery when compared with the left coronary system (P < 0.001). Gradient variations between cardiac phases, heart rates, body mass index, and readers were low. Gradients in patients with lesions were significantly different (P < 0.021) than in patients considered normal by CTA. Conclusions-Measurement of contrast opacification gradients from temporally uniform coronary CTA demonstrates feasibility and reproducibility in patients with normal coronary arteries. For all patients, the gradients defined with respect to the coronary lumen cross-sectional area and short-axis diameters are highly linear, not significantly influenced by the coronary artery (left anterior descending artery versus left circumflex versus right coronary artery), and have only small variation with respect to patient parameters. Preliminary evaluation of gradients across coronary artery lesions is promising but requires additional study. (Circ Cardiovasc Imaging. 2010;3:179-186.)
Intensity inhomogeneity is a great challenge for automated organ segmentation in magnetic resonance (MR) images. Many segmentation methods fail to deliver satisfactory results when the images are corrupted by a bias field. Although inhomogeneity correction methods exist, they often fail to remove the bias field completely in knee MR images. We present a new iterative approach that simultaneously predicts the segmentation mask of knee structures using a 3D U-net and estimates the bias field in 3D MR knee images using partial convolution operations. First, the test images run through a trained 3D U-net to generate a preliminary segmentation result, which is then fed to the partial convolution filter to create a preliminary estimation of the bias field using the segmented bone mask. Finally, the estimated bias field is then used to produce bias field corrected images as the new inputs to the 3D U-net. Through this loop, the segmentation results and bias field correction are iteratively improved. The proposed method was evaluated on 20 proton-density (PD)-weighted knee MRI scans with manually created segmentation ground truth using 10 fold cross-validation. In our preliminary experiments, the proposed methods outperformed conventional inhomogeneity-correction-plus-segmentation setup in terms of both segmentation accuracy and speed.
Segmentation of various structures from the chest radiograph is often performed as an initial step in computer-aided diagnosis/detection (CAD) systems. In this study, we implemented a multi-task fully convolutional network (FCN) to simultaneously segment multiple anatomical structures, namely the lung fields, the heart, and the clavicles, in standard posterior-anterior chest radiographs. This is done by adding multiple fully connected output nodes on top of a single FCN and using different objective functions for different structures, rather than training multiple FCNs or using a single FCN with a combined objective function for multiple classes. In our preliminary experiments, we found that the proposed multi-task FCN can not only reduce the training and running time compared to treating the multi-structure segmentation problems separately, but also help the deep neural network to converge faster and deliver better segmentation results on some challenging structures, like the clavicle. The proposed method was tested on a public database of 247 posterior–anterior chest radiograph and achieved comparable or higher accuracy on most of the structures when compared with the state-of-the-art segmentation methods.
In this report, an automatic method for segmentation of the pelvis in three-dimensional (3D) computed tomography (CT) images is proposed. The method is based on a 3D U-net which has as input the 3D CT image and estimated volumetric shape models of the targeted structures and which returns the probability maps of each structure. During training, the 3D U-net is initially trained using blank shape context inputs to generate the segmentation masks, i.e. relying only on the image channel of the input. The preliminary segmentation results are used to estimate a new shape model, which is then fed to the same network again, with the input images. With the additional shape context information, the U-net is trained again to generate better segmentation results. During the testing phase, the input image is fed through the same 3D U-net multiple times, first with blank shape context channels and then with iteratively re-estimated shape models. Preliminary results show that the proposed multi-pass U-net with iterative shape estimation outperforms both 2D and 3D conventional U-nets without the shape model.
A new level-set based interactive segmentation framework is introduced, where the algorithm learns the intensity distributions of the tumor and surrounding tissue from a line segment drawn by the user from the middle of the lesion towards the border. This information is used to design a likelihood function, which is then incorporated into the level-set framework as an external speed function guiding the segmentation. The endpoint of the input line segment sets a limit to the propagation of 3D region, i.e., when the zero-level-set crosses this point, the propagation is forced to stop. Finally, a fast level set algorithm with coherent propagation is used to solve the level set equation in real time. This allows the user to instantly see the 3D result while adjusting the position of the line segment to tune the parameters implicitly. The "fluctuating" character of the coherent propagation also enables the contour to coherently follow the mouse cursor's motion when the user tries to fine-tune the position of the contour on the boundary, where the learned likelihood function may not necessarily change much. Preliminary results suggest that radiologists can easily learn how to use the proposed segmentation tool and perform relatively accurate segmentation with much less time than the conventional slice-by-slice based manual procedure.
This paper presents one of the participating methods to the intervertebral disc segmentation challenge organized in conjunction with the 3rd MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging - MICCAI-CSI2015. The presented method consist of three steps. In the first step, vertebral bodies are detected and labeled using integral channel features and a graphical parts model. The second step consists of image registration, where a set of image volumes with corresponding intervertebral disc atlases are registered to the target volume using the output from the first step as initialization. In the final step, the registered atlases are combined using label fusion to derive the final segmentation. The pipeline was evaluated using a set of 15 + 10 T2-weighted image volumes provided as training and test data respectively for the segmentation challenge. For the training data, a mean disc centroid distance of 0.86 mm and an average DICE score of 91% was achieved, and for the test data the corresponding results were 0.90 mm and 90%.
Object: A new software module for coronary artery segmentation and visualization in CT angiography (CTA) datasets is presented, which aims to interactively segment coronary arteries and visualize them in 3D with maximum intensity projection (MIP) and volume rendering (VRT). Materials and Methods: The software was built as a plug-in for the open-source PACS workstation OsiriX. The main segmentation function is based an optimized "virtual contrast injection" algorithm, which uses fuzzy connectedness of the vessel lumen to separate the contrast-filled structures from each other. The software was evaluated in 42 clinical coronary CTA datasets acquired with 64-slice CT using isotropic voxels of 0.3-10.5 mm. Results: The median processing time was 6.4 min, and 100% of main branches (right coronary artery, left circumflex artery and left anterior descending artery) and 86.9% (219/252) of visible minor branches were intact. Visually correct centerlines were obtained automatically in 94.7% (321/339) of the intact branches. Conclusion: The new software is a promising tool for coronary CTA post-processing providing good overviews of the coronary artery with limited user interaction on low-end hardware, and the coronary CTA diagnosis procedure could potentially be more time-efficient than using thin-slab technique.
Purpose: The level-set method is known to require long computation time for 3D image segmentation, which limits its usage in clinical workflow. The goal of this study was to develop a fast level-set algorithm based on the coherent propagation method and explore its character using clinical datasets. Methods: The coherent propagation algorithm allows level set functions to converge faster by forcing the contour to move monotonically according to a predicted developing trend. Repeated temporary backwards propagation, caused by noise or numerical errors, is then avoided. It also makes it possible to detect local convergence, so that the parts of the boundary that have reached their final position can be excluded in subsequent iterations, thus reducing computation time. To compensate for the overshoot error, forward and backward coherent propagation is repeated periodically. This can result in fluctuations of great magnitude in parts of the contour. In this paper, a new gradual convergence scheme using a damping factor is proposed to address this problem. The new algorithm is also generalized to non-narrow band cases. Finally, the coherent propagation approach is combined with a new distance-regularized level set, which eliminates the needs of reinitialization of the distance. Results: Compared with the sparse field method implemented in the widely available ITKSnap software, the proposed algorithm is about 10 times faster when used for brain segmentation and about 100 times faster for aorta segmentation. Using a multiresolution approach, the new method achieved 50 times speed-up in liver segmentation. The Dice coefficient between the proposed method and the sparse field method is above 99% in most cases. Conclusions: A generalized coherent propagation algorithm for level set evolution yielded substantial improvement in processing time with both synthetic datasets and medical images.
To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e., coherent propagation. This strategy avoids the contour's local wiggling behavior which often occurs during the propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will, instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that prevents the endless computation in points that have reached the vessel border. As these points' expanding/shrinking trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved on 3D data with almost no loss of segmentation accuracy.
Purpose: The aim of this study was to develop an efficient CT scan range estimation method that is based on the analysis of image data itself instead of metadata analysis. This makes it possible to quantitatively compare the scan range of two studies. Methods: In our study, 3D stacks are first projected to 2D coronal images via a ray casting-like process. Trained 2D body part classifiers are then used to recognize different body parts in the projected image. The detected candidate regions go into a structure grouping process to eliminate false-positive detections. Finally, the scale and position of the patient relative to the projected figure are estimated based on the detected body parts via a structural voting. The start and end lines of the CT scan are projected to a standard human figure. The position readout is normalized so that the bottom of the feet represents 0.0, and the top of the head is 1.0. Results: Classifiers for 18 body parts were trained using 184 CT scans. The final application was tested on 136 randomly selected heterogeneous CT scans. Ground truth was generated by asking two human observers to mark the start and end positions of each scan on the standard human figure. When compared with the human observers, the mean absolute error of the proposed method is 1.2 % (max: 3.5 %) and 1.6 % (max: 5.4 %) for the start and end positions, respectively. Conclusion: We proposed a scan range estimation method using multiple body parts detection and relative structure position analysis. In our preliminary tests, the proposed method delivered promising results.
Background: Thanks to the development of computed tomography (CT) scanners and computer software, accurate coronary artery segmentation can be achieved with minimum user interaction. However, the question remains whether we can use these segmented images for reliable diagnosis. Purpose: To retrospectively evaluate the diagnostic accuracy of coronary CT angiography (CCTA) using segmented 3D data for the detection of significant stenosis. Material and Methods: CCTA data-sets from 30 patients were acquired with a 64-slice CT scanner and segmented using the region growing (RG) method and the "virtual contrast injection" (VC) method. Three types of images of each patient were reviewed by different reviewers for the presence of stenosis with diameter reduction of 50% or more. The evaluation was performed on four main arteries of each patient (120 arteries in total). For the original series, the reviewer was allowed to use all the 2D and 3D visualization tools available (conventional method). For the segmented results from RG and VC, only maximum intensity projection was used. Evaluation results were compared with catheter angiography (CA) for each artery in a blinded fashion. Results: Overall, 34 arteries with significant stenosis were identified by CA. The percentage of evaluable arteries, accuracy and negative predictive value for detecting stenosis were, respectively, 86%, 74%, and 93% for the conventional method, 83%, 71%, and 92% for VC, and 64%, 56%, and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (P < 0.01), whereas there was no significant difference in accuracy between the VC method and the conventional method (P = 0.22). Conclusion: The diagnostic accuracy for the RG-segmented 3D data is lower than those with access to 2D images, whereas the VC method shows diagnostic accuracy similar to the conventional method.
Purpose To enhance the functional expandability of a picture archiving and communication systems (PACS) workstation and to facilitate the integration of third-part image-processing modules, we propose a browser-server style method. Methods In the proposed solution, the PACS workstation shows the front-end user interface defined in an XML file while the image processing software is running in the background as a server. Inter-process communication (IPC) techniques allow an efficient exchange of image data, parameters, and user input between the PACS workstation and stand-alone image-processing software. Using a predefined communication protocol, the PACS workstation developer or image processing software developer does not need detailed information about the other system, but will still be able to achieve seamless integration between the two systems and the IPC procedure is totally transparent to the final user. Results A browser-server style solution was built between OsiriX (PACS workstation software) and MeVisLab (Image-Processing Software). Ten example image-processing modules were easily added to OsiriX by converting existing MeVisLab image processing networks. Image data transfer using shared memory added <10 ms of processing time while the other IPC methods cost 1-5 s in our experiments. Conclusion The browser-server style communication based on IPC techniques is an appealing method that allows PACS workstation developers and image processing software developers to cooperate while focusing on different interests.
An automatic seeding method for coronary artery segmentation and skeletonization is presented. The new method includes automatic removal of the rib cage, tracing of the ascending aorta and initial planting of seeds for the coronary arteries. The automatic seeds are then passed on to a �virtual contrast injection� algorithm performing segmentation and skeletonization. In preliminary experiments, most main branches of the coronary tree were segmented and skeletonized without any user interaction.
An automatic multi-organ segmentation method using hierarchical-shape-prior guided level sets is proposed. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that major structures with less population variety are at the top and smaller structures with higher irregularities are linked at a lower level. The segmentation is performed in a top-down fashion, where major structures are first segmented with higher confidence, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also provides extra cues to guide the segmentation of the lower-level structures. The proposed method was combined with a novel coherent propagating level set method, which is capable to detect local convergence and skip calculation in those parts, therefore significantly reducing computation time. Preliminary experiment results on a small number of clinical datasets are encouraging; the proposed method yielded a Dice coefficient above 90% for most major organs within a reasonable processing time without any user intervention.
An automatic multi-organ segmentation pipeline is presented. The segmentation starts with stripping the body of skin and subcutaneous fat using threshold-based level-set methods. After registering the image to be processed against a standard subject picked from the training datasets, a series of model-based level set segmentation operations is carried out guided by hierarchical shape priors. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, starting with ventral cavity, and then divided into thoracic cavity and abdominopelvic cavity. The third level contains the individual organs such as liver, spleen and kidneys. The segmentation is performed in a top-down fashion, where major structures are segmented first, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also constrains the segmentation of the lower-level structures. In our preliminary experiments, the proposed method yielded a Dice coeffcient around 90% for most major thoracic and abdominal organs in both contrastenhanced CT and non-enhanced datasets, while the average running time for segmenting ten organs was about 10 minutes.


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5,500 SF Prime Free Standing Flex Building
86 West Street in Waltham is a free standing flex building located directly off of Rt. 128/ I-95. the building features a mixture of office space, warehouse and lab space which can be reconfigured in a variety of different ways.
- 5,500 SF Office/Warehouse/Lab
- 12' Clear Height
- Tailboard & Drive-in Loading
- Lab has dedicated water, HVAC, and venting
Address: 86 West Street
City: Waltham
State/County: Greater Boston, Massachusetts, Metro West
Zip: 02451
Country: United States
Property Id : 21084
Property Size: 5,500 SF2
Building Size: 5,500 SF
Total Space Available: 5,500 SF
Stories: 1
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Elizabeth Holmes
Director of Corporate Services
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325 E. Florence Blvd.
Store Temporarily Closed
Donation Door Hours
Still Accepting donations
9am – 5pm
Job Center | Temporarily Closed
CLICK HERE : For a list of online resources and assistance programs
Share Your Experience
Our Casa Grande location is also a donation center and FREE Job Connection Center. We want to provide not only great bargains and finds to the community but much needed employment services as well. We also house a Linkages resource center in the location. With an amazing selection of housewares and clothing it is easy to shop guilt free and give back to the community. Your donations and purchases help us fully fund our Job Connection Centers and provide employment to over 500 individuals in Southern Arizona. Thank you for your support, we hope to see you soon.
Casa Grande Job Connection Center
Our Casa Grande thrift store has a free Job Connection Center and training room at the back of the retail space. The job center is staffed by one of our Goodwill Career Development Specialist that is equipped to assist the public with a wide range of job related needs. Resumes, cover letters, online applications, online job search, and even assistance with access to resources like transportation, clothing, food, and shelter. At Goodwill we believe a job can be transform the life of the job seeker but also the lives of their family members. Come in and see if we can help jump start your career. Learn more on our JOB CONNECTION CENTER page. We also have a Linkages office in this Goodwill store, they specialize in finding resources and work for individuals with disabilities.
What can you find at our Goodwill Thrift Stores?


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Villabuena de Álava, a small village with 300 inhabitants, lies on both slopes of a ravine carved out by a small creek. You could drive by the village on the main road and probably miss it completely if you weren't looking carefully. But if you turn off the road and drive down the hill into the village, you are in for a pleasant surprise, because this village has more than 40 wineries, a fact that surely deserves a place in the Guinness Book of Records.
Twelve of these wineries, along with the spectacular Hotel Viura, have created an innovative wine tour through the village that won a regional 2019 'Best Of Wine Tourism' award from the Great Wine Capitals Global Network.
We decided to take the tour to find out for ourselves.


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Police Search For Young Man And Woman After Daylight Killing
Hunt on for pair aged in their 20s
NSW Police are searching for a young man and woman after a 30-year-old man was killed in broad daylight on a Sydney street on Friday.
It is believed the man suffered a serious head injury around midday, in the inner-west suburb of Forest Lodge.
Witnesses told police a man and woman were involved in an altercation with the victim, and police said the pair were seen running from the scene. Police quickly arrived and sealed the area, mounting a large-scale ground and air search.
"Police seized a firearm and a number of weapons; however, no shots were fired. They will undergo forensic examination," police said on Saturday.
Police have formed Strike Force Hedda to investigate the incident, and also released descriptions of two people they want to speak to in connection with the death.
"Police would like to speak to a woman described as being of Caucasian appearance, aged in her early 20s, of thin build, about 178cm tall, with bleached blonde hair and dark coloured roots. She was last seen wearing a black singlet and maroon three-quarter pants," NSW Police said in a statement.
"Also, a man described as being of Caucasian appearance, aged in his late 20s, of medium build, about 178cm tall with short brown hair."
Anyone with information about this incident is urged to contact Crime Stoppers: 1800 333 000 or on the website.


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Extremely accurate high quality reproduction of the Patek Philippe Aquanaut Luce 5068R in Rose Gold.
Size: 35mm x 9.5mm
Movement: Ronda quartz movement
Decoration: Platinum plated movement close to the Genuine movement
Functions: Hours, minutes, seconds and date display
Case: 18k rose gold plated solid 316L stainless steel case
Crystal: Scratch-proof sapphire crystal
Dial: Black textured dial
Bezel: Diamond bezel
Strap: Rubber strap
Clasp: Deployant clasp


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It may still be August but I'm feeling autumnal.
It could be getting the boys ready to go back to school – buying the boys new shoes and stationary – the smell of new books and pens and paper – you can't beat it.
Or, it could be that we've been picking blackberries this week – a job I always associate with my nanna and stepping into autumn. Walking the dogs and then picking loads of berries, coats around our waists warm from walking, talking and seeing a bit of unexpected sunshine. Then she would turn her kitchen into a delicious smelling, purple, bubbling, sticky blackberry and apple jam factory and we would sink our teeth into great doorsteps of white bread smothered in the warm jam before it was shared out amongst the family.
Or, it could be that Madame Pickles AKA mum is on fire pickling, preserving and bottling anything that's edible. Remember the series The Vicar of Dibley? Marmite and banana cake? Only joking mum, you're a star and thanks for writing a guest post this week xxx
Or, I could be feeling autumnal because it's been cold and pouring down!!!
We have seen a bit of sun and managed to sit out under the stars. The sunflowers have peaked though, the rain has flattened them – luckily we got a few good photos of them first.
I shouldn't complain, it's been a cracking summer and it's not over yet – this is just a blip to keep the garden watered before a long Indian summer rolls in.
Toby and Nick's den is now officially their space and is complete with hammocks and a basket pulley system so they can move their stuff up and down. No craft workshop this week but a fun day up at Keith and Maylin's woodland. The kids and men were metal detecting and brass bullet shells were discovered.
We've also been to the waterfalls at Mortain
Had a laugh on les rosalies at Saint Fraimbault de Priéres
It all started off so well – Winnie trotting alongside us
and then she decided she had had enough. Paul had to get the 3 of us back on his own as there was no way I could pedal with a greyhound on my lap was there?!
Mandy and I went sampling some local produce at the apple farm in Sept-Forges
We went shopping for gifts – Mandy is in the UK this week with friends and she wanted to take some local presents back with her. As well as sampling calvados of various ages and other alcoholic apple delights we also had a tour
So the garden. The garden is exploding. We have organic veg galore at the moment and it all tastes so good. I have wondered if it was really worth all the effort – did I just say that out loud? It is effort, it is time consuming and you can buy all this stuff…..
I have never tasted tomatoes like these before, never bitten into such a sweet corn on the cob that needs no butter, never gorged on such flavoursome strawberries or had so much basil, rich basil at my finger tips. It is SO worth it. It's organic, seasonal, sellophane free and tastes AMAZING! Not only that but it looks so beautiful. The colours, the shapes. Chilli's that curl, carrot men, a fork full of potatoes – enormous monsters and tiny babies – why don't we want to see this stuff in the shops? Who decided vegetables had to be uniform?
We're a bit proud at 23littletrees and certainly getting our 5 a day.
This week I would like to end by paying my respects to Maurice Abrahams who was sadly killed in the Shoreham Airshow disaster. Maurice was a familiar face as I grew up. Very much part if the community, always happy and friendly. When Paul and I got married he was our chauffeur and really went the extra mile for us. We got married in Lewes and had a giggle as Maurice managed to get his huge Daimler limousine around the small roads and tiny roundabouts – we had to go around the block again as I arrived before the guests – they were spilling out of the pub! Then after the ceremony he stopped so we could have some photos taken in a corn field. As our guests drove past us on their way to the party they beeped and waved and Maurice surprised us with glasses and a bottle of fizz. A lovely man.


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Service Description
Safer Sphere was appointed as Principal Designer Advisor working with Redworth Associates to deliver the refurbishment of two existing theatres within a live hospital.
Under CDM 2015 the role is Principal Designer Advisors. The scope of service includes:-
- Principal Designer Advisor services in line with CDM 2015
- Assisting with H&S in designs, buildability and programme to reduce risk profiles
- Assisting the Client in discharging their duties under CDM
- Pre-Contract reviews with sub-contractors and packages
Service Challenges
Whilst the client and design team have led the design with best practice CDM principles, some of the key challenges include: –
- Providing expert advice on safe cleaning, maintenance and plant replacement strategies for the technically demanding building
- Working within a live hospital environment


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Servings: 2
Prep time: 15 minutes
Total time: 30 minutes
¼ ounce fresh mint
2 ounces fresh basil
¼ ounce fresh cilantro
1 rib celery
1 small yellow onion
1 jalapeño
2 garlic cloves
2 ounces kale
3 lemons
2 cups olive oil
5 ounces heirloom cherry tomatoes
2 ½ ounces Parmesan cheese, shaved
12 ounces flap meat
2 torta bread
2 tablespoons unsalted butter
1. Remove and discard the stems from the mint, basil, and cilantro. Roughly chop leaves and add to a large bowl. Dice the celery and onion into ¼-inch pieces, and add to the bowl as well. Finely chop 1 of the garlic cloves and add to the large bowl. Slice the other garlic clove in half lengthwise and set aside. Finely chop the kale. Add to the large bowl. Zest and juice 2 of the lemons and add to the bowl. Season with salt and pepper. Cover with the olive oil and stir to combine.
2. Halve the heirloom cherry tomatoes and place into a medium bowl. Add 2 teaspoons of olive oil and ¼ teaspoon each of salt and pepper to the bowl. Toss to combine. Add the Parmesan cheese. Zest the remaining lemon and add it to the bowl. Cut the lemon in half and squeeze the juice into the bowl. Toss to combine and set aside.
3. Pat dry the steak with paper towels. Season each steak with a ¼ teaspoon of each salt and pepper and rub with 1 tablespoon olive oil. Heat a large cast iron grill pan. When hot, add the steak to the pan and sear on each side for 3 minutes. Remove from pan and set on a plate to rest.
4. Slice the torta bread in half both lengthwise and widthwise. Drizzle with olive oil. Grill the inside of the bread until toasted. Remove the torta bread from the pan and rub the toasted sides of the bread with the halved garlic clove.
5. Slice the steak into ½-inch pieces. Layer the steak onto the toasted torta bread. Top each sandwich with 2 tablespoons of salsa verde. Close the sandwich with the remaining halves of the torta bread. Serve the heirloom tomato salad on the side.


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Official blog for "The Register" newsletter; containing articles and updates from the ATC about stewardship on the Appalachian Trail.
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During Wilderness Skills Institute 2015, field work included identifying locations for and the installation of rolling grade dips to control water by getting it off the Trail. As part of that training, Appalachian Trail Conservancy's Trail Specialist Chris Binder explained why soil science is important to establishing the Trail and its features.
In this video Chris explains how familiarity with soil types will help in the construction of rolling grade dips.
For more information about rolling grade dips refer to the USFS publication Trail Construction and Maintenance Notebook 2007 Edition, page 35. There is also more on laying out and building dips in A.T. Design Construction and Maintenance (2000), pages 63-64.
This was taught as part of the Essential Trail Maintenance course at the 2015 Wilderness Skills Institute, hosted by the Appalachian Trail Conservancy, U.S Forest Service, and Southern Appalachian Wilderness Stewards.
Blogger: Patrick Slaughter
Title: Trail Skills Intern, Appalachian Trail Conservancy
Based out of Asheville, NC
Affiliation: Landscape Architecture student, North Carolina A&T State University
in Greensboro, NC
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Hope you got an complete idea about how to start a bitcoin exchange site, If you have further more clarification, Our tech team will always ready to connect with you and you can reach us at any time, through whatsapp or telegram. Most of the people started spending their lot of time in learning programming and ended up in failure. I ) Bitcoin Exchange : Creation Phase. Industrial name of bitcoin is BTC. There are a lot of service providers, bitcoin exchange development companies are available online, and many shopping sites sell bitcoin exchange plug-ins, templates, and add-ons. During this process you have to analyze the current trend of cryptocurrency trading industry, you have to check the existing popular exchanges and their services. Analyze the scope interessante bitcoins of your bitcoin exchange website. There is always two phases in running a successful bitcoin exchange website eation.
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Lets discuss the core concept of this article! Coinjoker is also expert in introducing customized cryptocurrency exchange bitcoin trading website script script with top most trending modules like liquidity, white-label solution, ICO and blockchain technology and. Get Started, technology Stack, having 4 Yrs of experience in crypto industry, We build our software to perform scale. Buyer, seller, the Admin, the above persons are the main players who keep an exchange always active. Optimization, this is not a onetime process. Following are the most recognizable business models which are found to be highly traffic generating trading features for a bitcoin exchange site.
In business point of view, admin is the only person who gains a lot of profit than the bitcoin traders. Regardless of these things, you may figure out open-source code and fix a large portion of the bugs and include interpretations, after quite a while you will be exhausted doing that and that will cause you in inconveniences. Deal with a right Bitcoin exchange script/ Software Service provider. If you are searching for readymade and white label bitcoin exchange script service provider bitdeal is ready to assist you! Do you have a big plan to start your own bitcoin exchange website, but stuck up with getting a right solution? Home, blog, bitcoin Exchange Script, bitcoin Exchange Script, oct 30, 2018. Flexible UI, Secured protocols, Customizable. As I said above it is not a onetime process, you just have to do it often, to beat the competition. Many countries allow bitcoin exchange business and trading with bitcoin, but there are some exceptions, especially India and china have recently banned bitcoin transactions. Live Demo, Instant Launch, and Free consultation to launch your crypto exchange platform. If you want to launch your bitcoin exchange website better choose a readymade and white label bitcoin exchange script, because it will not require multiple layer of optimization, So that you can launch your website instantly. Bitcoin Exchange Script is offered by coinjoker who is expert in cryptocurrency exchange and trading business solutions like Whitelabel, Liquidity, Blockchain Technology and Peer2Peer Exchange. To Start a bitcoin exchange site you never have to skip any of the steps below!
Etc So if you want to make your bitcoin exchange more user friendly make sure you have added all these features in bitcoin trading website script your bitcoin exchange website. It is quite difficult. Steps in creating your own bitcoin exchange website. List out your requirements. Based on your requirements make a choice on your own and start proceeding further.
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Cryptocurrency Exchange Platform, it is a website that executes cryptocurrency trading between any user or peer. Trader, who buy or sell crypto currencies for Fiat bitcoin trading website script currencies or for any other cyrptos. Before proceeding in to further, teach your mind that you are going to construct a big trading platform that would ease the trading of cryptocurrencies between any peer to peer. Decentralized Trading With Atomic Swapping Margin Trading Lending Bitcoin SIP Ethereum Trading P2P Trading Admin Based Trading Digital Token / Token Trading New Coin listing Features. Exchange plug-ins/c Ready Made bitcoin Exchange Script Features: Security Two Factor authentication KYC To Identify customers in order to prevent fraudulence AES Encryption Prevention Anti csrf Token XSS Clean Software / System details disclosure Enabled With Debug Mode Google ReCaptcha.
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The script should be fulfilled with some of the basic components Basic components that of a bitcoin exchange script Bitcoin wallet. Ensure the licensing and bitcoin exchange regulations of the country you decide to launch. After this you have to decide how uniquely you are going to present the trading features for your beloved traders. All you need is a right development support either it may be a bitcoin exchange development company, freelancers, or a complete bitcoin exchange script source code. Establish the liquidity options, integrate high security features, discuss with developer team of the bitcoin exchange website development company to escalate the optimizations. Step 6 : Keep connected with the team of developer from the company you choose This would help you to freely escalate all your ideas, suggestions, and other requirements to the company. (i.e) it would not have a physical appearance but can be traded or exchanged via any trading platform without the control of central banks or any other government authorities.
Step 5: Establish Liquidity Liquidity is the main thing to run your bitcoin exchange successfully. How to start a bitcoin exchange Website? Our cryptocurrency exchange script powers several bitcoin exchange trading software website. Coinsclone is the Local, bitcoin clone script software provider and specialized in this area. It offers the script with escrow admin, secure wallet services, local trading, user-admin dashboard, offline trading, liquidity and more. Script, Localbitcoins Clone PHP Script, Remitano Clone Script, Escrow script with highly secured features at reasonable cost. Our script helps you to start the bitcoin exchange website like Localbitcoins, remitano. Bitcoin Trading, script, pHP to support enhanced cryptocurrency trading with unlimited altcoins. Start building your own bitcoin trading platform with bitdeal.
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Strategie me bt run (diskren mechanická nebo pln automatická (takzvan automatick obchodn systém - AOS). How to start your own bitcoin exchange website? Coinjoker-, bitcoin Trading, script, pHP is a secure and reliable fully advanced bitcoin and cryptocurrency trading solution and software. If you see price increasing and the macd recording lower highs, then you have a bearish divergence. We will discuss this in more detail later, but as a preview, the size of the histogram and whether the macd is above forex below zero speaks to the momentum of the security. Forex zpravodajstv je zna portálu, kde najdete nejnovj zprávy ze svta forexu, stejn tak jako forex zone nebo vzdlávac zna. So you cant really say that indicators are wrong. Get a complete instructional guide to start your own bitcoin exchange website. If you trade during the night sideways market, make sure that you have available economic calendar or customize economic news alerts.
Liquidity refers to how active a market. Ethereum, introduced in 2013 by then 19-year-old Vitalik Buterin, on the surface. How to start a bitcoin exchange website. Whether you prefer Advanced PHP you can still take your pick! Allerdings ist eine Nominierung einer Person mit ungeklärter Identität nicht möglich. In der chinesischen Stadt Dalian hoch in den Bergen stehen die größten Bitcoin-Mining-Anlagen. Eine funktionsfähige Einheit für zuhause kostet rund.000 Euro. Broker Vergleich news » ETF auf Bitcoins 2018 interessantes Investment für Privatanleger. Obviously, the most liquid asset of all is cash. Ethereum also protects your project from outside influences, while its decentralized network means that you won't face downtime. Live Chart Einen guten Überblick über die aktuellen Kurse der Kryptowährungen bietet cryptowatch. In an annual speech on Tuesday outlining his priorities for the coming year, Trump said illegal immigration was a national crisis and reiterated his vow to build a border wall.
This trading system publisher took. Strategy will both enter and exit the market forex when we receive a signal from the macd, confirmed by a signal from the. Transparent, direkt und günstig. Neben der beiden Börsen Coinsetter (New York) und CleverCoin (Niederlande vereinnahmte Kraken Ende 2016 mit Glidera einen weiteren großen Player der Bitcoin-Branche. Market Liquidity, market liquidity refers to the extent to which a market, such as a country's stock market or a city's real estate market, allows assets to be bought and sold at stable, transparent prices. Local bitcoin exchange script available for your Bitcoin Trading business solutions ( Wallet, Security, Escrow services ). The latest news and headlines regarding Natural Gas Futures prices, market changes bitcoin trading website script and more. This has allowed less time in front of the computer without an adverse affect on returns. Macd are two ways you can pronounce macd.
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When the spread between the bid and ask prices grows, the market becomes more illiquid. Exotic forex pairs and liquidity Exotic currency pairs comprise of a major pair being traded alongside the currency of a developing or emerging market such as the Mexican peso, Hong Kong dollar or the Turkish Lira. Traditioneller Markt, die Korrelation von Bitcoin zu den betrachteten traditionellen Märkten ist weiterhin sehr schwach und zu jedem einzelnen Asset antikorreliert. Jetzt kostenlos testen, mehr zum Thema. No one knows who that is to the date, as the creator. This should taper off. Set for biggest monthly. Youd simply look to short rallies during a macd downtrend and buy pullbacks during an uptrend. Im August 2015 wurde Mark Karpeles, der frühere Chef von. One of the reasons for sharp price surges ( spikes ) in both directions during high-impact news bitcoin trading website script releases is the absence of liquidity providers, which simply dont want to risk at this time by offsetting positions over releases of the news. The more you risk the more you lose or you end of day trading system forex can make tipos de brokers forex more.
Market liquidity explained, liquidity describes the extent to which an asset can be bought and sold quickly, and at stable prices. Any time frame as well as any currency pair can be used. Schließlich haben Bitcoins ähnlich wie Aktien nur den Wert, den Menschen ihnen beimessen. Um sich danach innerhalb von zwei Monaten zu verdoppeln. When this happens, price is usually in a range setting up a possible break out trade.
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Macd - 5 Profitable Trading Strategies. Binance Clone, script - Start your own bitcoin cryptocurrency exchange website like binance to support trading with countless number of cryptocurrencies. For instance, the LTC hard cap is 84 million, and it can provide 4x faster transaction times than. Coinjoker - Bitcoin trading platform script is provides a distributed ledger platform with advanced bitcoin trading website script solutions like ICO, Smart Contracts, Trading bot and Token creation which is making revolution in the blockchain business industries. Despite experiencing high levels of liquidity, the forex market does not exhibit stable pricing. Heute sind es rund.600 Dollar. Jährlich feiern Anhänger der Digitalwährung seitdem. Bitcoin exchange script and bitcoin exchange website script to create your own bitcoin exchange website. We deal in diverse range of clone scripts to create the best bitcoin trading software.
Leider steht Bitcoin damit, über einen Monat gemittelt, immer noch schlechter als die Vergleichsassets: Bis auf diese Performance bleibt Bitcoin jedoch als Ergänzung zu traditionellen Portfolios interessant: Seit einiger Zeit ist die Kopplung zu den klassischen Märkten sehr gering und auch die Volatilität sinkt weiter. Advanced forex thai baht forex trading strategies, click end of day trading system forex here. Zwei Männer, die Mining-Equipment verkauften, wurden festgenommen. Shane started day trading Forex but has since transitioned to a swing/position focus in most markets including commodities and futures. These liquid stocks are usually identifiable by their daily volume, which can number millions of shares, or even hundreds of millions. Ein Überblick über die nötigen Produkte: hier. Liquid as that sounds, it's not a drop in the bucket compared to Intel (intc which led the Nasdaq that day, with a volume.5 million sharesor to Ford Motor (F which led the New York Stock Exchange. The first major example of a Dapp is Cryptokitties, which took the world by storm in in the end of 2017. If I post trades, Ill send out another email, but for intraday charts, I'll just put them on here. Investors, then, will not have to give up unrealized gains bitcoin trading website script for a quick sale.
Liquidity is a possibility to, liquidate an asset quickly and without affecting its price dramatically. Let's take a glance at the very basis of currencies trading with macd indicator. M It is one of topmost Blockchain Software development company provides sufficient security solution to build hyip bitcoin investment business website around the world. The 2 line cross can be a very powerful indicator of trading potential in the market. Trackback-URL, permalink, trackbacks / Kommentare, ihr Kommentar: Schreiben Sie Ihre Meinung, Erfahrungen, Anregungen mit oder zu diesem Thema.
In forex, liquidity matters because it tends to reduce the risk of slippage, gives faster execution of orders and tighter bid-offer spreads. The bitcoin trading website script longer-term trend line comes in.3925, and for short positions, thats the big target. Für das Cashout von Bitcoins gibt es viele Methoden. TK, jeho obsah je také chránn autorskm zákonem. Krom svého vlastnho obsahu vyuvá i zpravodajstv. Forex broker je zprostedkoval pro obchodován mezi forexem a forexovm obchodnkem. The Basics of Liquidity, cash is considered the standard for liquidity because it can most quickly and easily be converted into other assets. Here is the complete guide to study and kickstart your own Bitcoin exchange platform. Lookback period for the slow line (26). "Jordan's comments have set the tone for the franc move this week and I think the franc's weakness has certainly more room to run Thomas Flury, head of currency strategy at UBS Wealth Management's chief investment office, said Friday, according to Reuters. Get latest blog updates of Bitcoin Exchange Script. Volatility is the measure of how drastically a markets prices change. Von 450 im August 2014 auf.000 im November 2016.
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Die Suche nach dem Erfinder war auch deswegen so spannend, weil ihm eine Summe von einer Million Bitcoins nachgesagt wurde. Bitcoin Ninjas 'Ninja Signals' Buy/Sell Alerts Backtesting TradingView Script (for Cryptocurrencies, Forex, GunBot, ProfitTrailer, automatic trading software, and more). This opçes binárias análise técnica requires the assistance of the well-known Awesome Oscillator. CFDs, spread bets, futures, ETFs and etns it is easier to trade commodities than ever before. If there is enough trading volume at the next price, a trade will be executed without a slippage.
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This means that pairs like. Again, keep in mind the lagging nature of all indicators with this trading method and highly consider using multiple bitcoin trading website script time frames for your trading. The author has made every effort to ensure accuracy of information provided; however, neither Kitco Metals Inc. Now that we understand the basics of the macd macd, let's dive into five simple strategies you can test out. Market liquidity is important for a number of reasons, but primarily because it impacts how quickly you can open and close positions. Die Digitalwährung soll unabhängig machen, entwickelt worden ist die Digitalwährung nämlich eigentlich als Reaktion auf die Finanzkrise. Wenn Ihnen keiner mehr Bitcoins abnimmt, können Sie nichts damit anfangen, sagt Thiele. In investment terms, assessing accounting liquidity means comparing liquid assets to current liabilities, or financial obligations that come due within one year. The moves were equally violent in the bond market with shorter-dated Australian government bond yields set for their biggest daily drop in more than two years. One of our traders from Australia was keen on Day Trading Forex using Real-Time Charts. Liquidity is a possibility to, liquidate an asset quickly and without affecting its price dramatically.


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Freely in Hope invites you to the Advocates Society Launch Party! Learn more about how you can help a survivor of sexual violence reach their academic dreams! The Advocates Society is for those who want to make a difference in the world by standing with survivors. Learn more here. Featuring artwork created by Freely in Hope Scholars. Join us with special …View Post
Dec 26
2018 Year End Highlights
Look at all we did together in 2018! Make your tax-deductible gift count for 2018 by donating online by December 31! We piloted our first sexual violence prevention curriculum, called ENEZA, in one high school in Kibera. The program was so successful that we are expanding it in 2019! Through ENEZA, we have reached over 1,333 students and children in …View Post
THANK YOU FOR CELEBRATING International Day of the Girl WITH US Thanks to you and Me to Her, we were able to throw our International Day of the Girl Conference for 300 girls from Kibera! Though the project was not fully funded, we changed a few things in the program to ensure that the day was an inspirational and empowering time for …View Post
Dear Friends of Freely in Hope, On Saturday, August 25, 2018, we will be hosting our 8th Anniversary Gala at Dogpatch Studios in San Francisco! Over 240 guests from all over the Bay Area will join us for a special night of generosity and celebration — marking our success over the past eight years of equipping survivors of sexual violence to lead in …View Post
Category: Blog, Events, Uncategorized
For Women's Month, Freely in Hope celebrated the unique voices of our survivors and advocates! Each person plays a unique role in this ongoing battle against sexual violence. Jewelry, a symbol of individuality and self-expression, was a focus of our most recent event: "Women's Voice" hosted by Maggie Gunn in San Francisco! As an advocate for Freely in …View Post
Category: Blog, Events, Leadership
Apr 2
Advocates Society
"I would like all the women who have gone through sexual and gender-based violence to speak out about their experiences as a way healing their wounds, to encourage other women who are going through this same trauma, and to find justice." -Eunice, Social Worker & Counselor This month, in celebration of International Women's Day, we launched Advocates Society! As …View Post
Donate before midnight PST on December 31 to make your tax-deductible donation count in 2017! Thank you for generously supporting Freely in Hope's work this year! We are dancing with joy! – – 2017 Transformative Highlights This was truly the year of transformation for us. Take a look at everything that you helped us accomplish this year! 16 women in …View Post
When I was young, no one ever told me how great I can be. Nobody affirmed me of my gifts and strengths as a woman. Today, International Day of the Girl (IDG) helps reassure hundreds (even thousands) of girls—letting them know their dreams are valid… those dreams are important to me. For many of these girls, this day signifies the only …View Post
Dear Friends of Freely in Hope, On Saturday, August 26, 2017, we will be hosting our 7th Anniversary Gala at Dogpatch Studios in San Francisco! In addition to requesting your presence at this event, I humbly ask you, your corporation or your family to consider coming alongside us by being a Matching Sponsor for our Gala. Over 200 guests will …View Post
May 18
Art + Healing Workshop
ABOUT THE WORKSHOP Art has the ability to speak meaning beyond words—expressing unspoken pain, unseen wounds, the possibility of hope, and audacious dreams. In this workshop, engage in art practices, and processes of reflection that illustrate how the intersection of art and healing leads to transformation. The Art + Healing workshop is designed for those in search of healing for …View Post