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An Automatic Detection Method Of Cerebral Aneurysms In Time-Of-Flight Magnetic Resonance Angiography Images Based On Attention 3D U-Net

Background:Subarachnoid hemorrhage caused by ruptured cerebral aneurysm often leads to fatal consequences.However,if the aneurysm can be found and treated during asymptomatic periods,the probability of rupture can be greatly reduced.At present,time-of-flight magnetic resonance angiography is one of the most commonly used non-invasive screening techniques for cerebral aneurysm,and the application of deep learning technology in aneurysm detection can effectively improve the screening effect of aneurysm.Existing studies have found that three-dimensional features play an important role in aneurysm detection,but they require a large amount of training data and have problems such as a high false positive rate. Methods:This paper proposed a novel method for aneurysm detection.First,a fully automatic cerebral artery segmentation algorithm without training data was used to extract the volume of interest,and then the 3D U-Net was improved by the 3D SENet module to establish an aneurysm detection model.Eventually a set of fully automated,end-to-end aneurysm detection methods have been formed. Results:A total of 231 magnetic resonance angiography image data were used in this study,among which 132 were training sets,34 were internal test sets and 65 were external test sets.The presented method obtained 97.89% sensitivity in the five-fold cross-validation and obtained 91.0% sensitivity with 2.48 false positives/case in the detection of the external test sets. Conclusions:Compared with the results of our previous studies and other studies,the method in this paper achieves a very competitive sensitivity with less training data and maintains a low false positive rate.As the only method currently using 3D U-Net for aneurysm detection,it proves the feasibility and superior performance of this network in aneurysm detection,and also explores the potential of the channel attention mechanism in this task.


Introduction
Subarachnoid hemorrhage(SAH) caused by cerebral aneurysm often causes death or severe disability. Although it can cause such serious consequences, cerebral aneurysms do not appear suddenly. They usually have an incubation period of several years or even decades, during which they do not show any symptoms. [1] Treatments such as clipping and endovascular intervention when the cerebral aneurysm is not ruptured can get a better prognosis and significantly prolong the patient's survival. Therefore, regular aneurysm screening is performed and cerebral arteries are 3 / 20 found in the asymptomatic stage intervention or treatment of aneurysms in time is one of the effective ways to avoid aneurysm rupture. Time-Of-Flight magnetic resonance angiography(TOF-MRA) is currently one of the most commonly used methods for screening aneurysms. Because it has diagnostic accuracy similar to DSA and CTA [2,3], and is a non-invasive examination method, it is especially suitable for aneurysm screening when asymptomatic.
For radiologists, it has always been an arduous task to screen aneurysms quickly, massively, and accurately in scenarios such as physical examinations. Computer Assisted Detection methods provide a promising solution for aneurysm screening. [4][5][6][7][8][9][10][11][12] In this field, several works have been proposed in recent years. Joseph Stember et al. [11] detected 98.8%(85/86) basilar tip aneurysms in 1.5T and 3.0T TOF-MRA images. Faron Anton et al. [12] got 90% sensitivity at 6.1 False Positive(FPs)/case. Nakao Takahiro et al. [8] detected 94.2% (98/104) of aneurysms with 2.90 FPs/case, with sensitivity of 70.0% at 0.26 FPs/case. Ueda Daiju et al. [9] got 91% sensitivity at 6.60 FPs/case. In the above studies, researchers have found that three-dimensional features have an important impact on the performance of aneurysm detection methods. Faron et al. [12] used the Deepmedic framework [13] with three-dimensional convolution. Joseph et al. [11] and Nakao et al. [8] used a 2D CNN network, but their inputs were multi-angle slice sets for each sample block. The above works prove that the three-dimensional convolutional neural network is suitable for the task of cerebral aneurysm detection. However, the main disadvantage of 3D convolutional networks in application is that it is difficult to fully optimize the network hyperparameters with a small training data set. And most of the time, collecting a large amount of TOF-MRA image data and labeling it is a task that requires a lot of manpower. Besides, 3D convolutional networks will use more computing resources, often redundant, and more model parameters, which is not conducive to solving medical imaging related tasks. Attention mechanism was proposed to solve such problems. Among them, SKNet [14], SENet [15], and GCNet [16] are three representative attention models.
In the research of our last paper [17], we proposed an automated computer assisted detection system for cerebral aneurysms using an improved 3D U-Net [18][19][20]. In which, the method achieved 82.9% sensitivity at 0.86 FPs/case, with 76 cases as training dataset. To improve the performance of the method, in this paper, we compared the effects of adding different attention modules to the original network, and further optimized the best solution. At the same time, we also increased the amount of data in the dataset to improve the performance of the method.

Materials
The ethics board of our institution comprehensively re-viewed and approved the protocol of this study. Two of the authors of this paper (D.R. and W.D.) are radiologists with 4 years of work experience. They annotated all the aneurysms in this study, with the DSA as ground truth.

Datasets
A total of 231 patients (all have un-ruptured cystic aneurysm) underwent contrast unenhanced 3.0T 3D TOF-MRA. In this study, angiography examinations were performed with two 3.0T system, GE Discovery MR750 and SIMENS Verio, using two imaging factors separately. On GE MR750 the factors were: repetition time/echo time, 25msec/5.7msec; flip angle, 20°; field of view, 220mm; section thickness, 1.2mm; acquisition matrix, 320256, reconstructed to 10241024240; acquisition time, 2min14s; and on SIMENS Verio, the factors were: repetition time/echo time, 22msec/3.6msec; flip angle, 18°; field of view, 240mm; section thickness, 0.5mm; acquisition matrix, 384254，reconstructed to 768536162. anterior cerebral artery area, posterior cerebral artery area, but no basilar artery area and vertebral artery area. The aneurysms' average size was 7.51mm in the internal carotid artery area, 5.49mm in the anterior cerebral artery area, 8.99mm in the middle cerebral artery area and 2.92mm in the posterior cerebral artery area, respectively. In the above areas, the largest aneurysm was located in the internal carotid artery area.

Development of the method
In our method, we designed two main steps: First, automatically extraction of the volume of interest(VOI) from the input image; Second, detection of the suspected aneurysm areas using deep neural networks. After completing the training of the deep neural network and obtaining the model, this method realized the automatic detection of cerebral aneurysms.

Step One: Extraction of the VOI
The input was DICOM datasets, which in the form of a volume. First, the image grayscale range was normalized to [0, 1024]. Then using a threshold based filter we filtered the image into a binary image(voxels with value 0 and 1). In this study, the threshold of the filter was 300. Took 60% of the slices in the middle of all slices in the Z-axis direction for the next step.  predicted to be an aneurysm, took the center of the area and calculate the maximum distance from the center to the boundary of the area. If the distance was not greater than 30 voxels in transverse section, drew a cube area with size 6060H as the prediction result (lengthwidthheight, length and width were the size in transverse section, and height refers to how many transverse-sectional slices the predicted area contains), else, the length and width were the same with the double of distance. Therefore, the cube predicted area obtained by the final processing was the detected area that might contain an aneurysm.

Results
We used sensitivity and false positive rates as indicators to evaluate the proposed method, which defined as below:
All the aneurysms were considered positive. As the result of the system was a cube area, if more than 30% of the aneurysm was in this cube area, then this cube area was considered a TP(True In the process of selecting the attention model, we tested three attention modules of SENet [15], SKNet [14], and GCNet [16]. Among them, SENet is based on the channel attention mechanism, SKNet is based on the convolution kernel attention mechanism, and GCNet combines the Non-local module with SENet. We tried to embed GCNet, SKNet, and SENet three attention modules in the baseline network respectively, and adopted three position deployment schemes: up-sampling path, down-sampling path, and up-down sampling conversion position. The performance of the baseline network after embedding the above three attention modules at different positions was tested. In order to improve the efficiency of selection and comparison, we used the training set data of the previous paper [17], and randomly selected 38 cases in the test set of this article as the external test set for testing. In order to compare the impact of the improvement of the verification method and the increase of the data set on the performance of the method, we used the data set of the previous paper and the data set of this paper to test the method of the previous paper and the method of this paper at the same time.  Figure 6: The undetected aneurysms in this study

Discussion and Conclusion
In this study, we designed an improved method flow for aneurysm detection, and studied the existing 3D U-Net and attention mechanism combination method, and finally improved the blood vessel extraction method, and embedded the SENet module into the baseline network. The data used in the study came from two 3.0T magnetic resonance equipment of different brands and models, and the acquisition factors of the TOF-MRA images used were also slightly different. After training the network and performing 5-fold cross-validation, the average sensitivity was 97.890.88%. The best model was selected and tested on the external test set. The sensitivity was 91.0% and the false positive rate was 2.48 FPs/case. in cases over 60 years old is slightly worse than other age subgroups. Compared with our previous work, the method proposed this time has improvements in sensitivity, generalization, and balance.
Excluding the influence of the number of cases in the data set, the method proposed this time still shows better performance. Prove the role of attention mechanism in the task of aneurysm detection.
Comparing with the research results of other researchers, it can be found that this method achieves a higher sensitivity while ensuring a lower false positive rate. Since a patient usually does not have more than 3 aneurysms, when the number of false positives exceeds 3 in the test result of a case, it may cause trouble to the doctor who diagnosed it. of features such as vessel length and density changes. The above problems can be solved by increasing the length measurement of similar voxels in multiple directions during the downsampling process, averaging the density value of the extraction result of the blood vessel region, and multi-channel input. Given that the performance improvement brought by only increasing the amount of training data is not significant, it can be considered that the effective reduction of network parameters by the channel attention module is the main reason for this improvement. Therefore, for the research content of this article, effective simplification of network parameters is still the primary research direction, and the network can be improved from reducing the number of network layers and optimizing the expression of global features in the network. We will continue to conduct indepth research on the above methods and hope to get better results.

Corresponding author
Correspondence to Li Yuxin and Geng Daoying.