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Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network

Purpose: To apply a convolutional neural network (CNN) to develop a system that segments intensity calibration phantom regions in computed tomography (CT) images, and to test the system in a large cohort to evaluate its robustness. Methods: A total of 1040 cases (520 cases each from two institutions), in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used, were included herein. A training dataset was created by manually segmenting the regions of the phantom for 40 cases (20 cases each). Segmentation accuracy of the CNN model was assessed with the Dice coefficient and the average symmetric surface distance (ASD) through the 4-fold cross validation. Further, absolute differences of radiodensity values (in Hounsfield units: HU) were compared between manually segmented regions and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate coefficients for the correlation between radiodensity and the densities of the phantom. Results: After training, the median Dice coefficient was 0.977, and the median ASD was 0.116 mm. When segmented regions were compared between manual segmentation and automated segmentation, the median absolute difference was 0.114 HU. For the test cases, the median correlation coefficient was 0.9998 for one institution and was 0.9999 for the other, with a minimum value of 0.9863. Conclusions: The CNN model successfully segmented the calibration phantom's regions in the CT images with excellent accuracy, and the automated method was found to be at least equivalent to the conventional manual method. Future study should integrate the system by automatically segmenting the region of interest in bones such that the bone mineral density can be fully automatically quantified from CT images.


Introduction
Quantification of bone mineral density (BMD) is necessary in the diagnosis of osteopenia and osteoporosis. Usually, lumbar vertebral or proximal femoral BMD is quantified using dual-energy X-ray absorptiometry-the procedure recommended by the World Health Organization and by several guidelines [1][2][3]. Yet, BMD assessment in other specific regions is also important as it can be used for surgical planning to achieve good clinical results; to this end, studies have used quantitative computed tomography (CT) images to determine local BMD in the proximal femur [4], femoral head [5,6], and the distal radius 7 same model of calibration phantom was used throughout the study, segmentation of the CT image using a simple rigid 3D phantom model definition was not possible. Instead, we employed Bayesian U-Net [11], a convolutional neural network for semantic segmentation. For the training dataset, we randomly selected 40 cases (20 cases from each hospital), and in each image, the calibration phantom was manually segmented using Synapse Vincent software (v4.4, Fujifilm, Tokyo, Japan).

Automated calibration and post-processing
Analysis of the convolution neural network's segmentation results was performed on the remaining 1000 cases (n=500 from each hospital). After the regions calibration phantom were defined (Fig. 2a), the segmented regions were eroded using a 3-pixel disk-shaped structuring element to avoid susceptibility to small variations at the boundaries that can affect the values of radiodensity measured in each material (Fig. 2b). Linear regression (the standard protocol suggested by the manufacturer) was applied to model the relationships between HU and mg/cm 3 for each test case (Fig. 2c); the slopes and correlation coefficients of the regression models were calculated. Matlab (v9.8, The MathWorks, Natick, MA, USA) was used for post-processing and the calibration process.

Quantitative assessment of segmentation accuracy
To assess segmentation accuracy, 4-fold cross validation was performed on the training dataset-15 cases from each hospital (n=30 total) were randomly selected for training and the remaining five cases from each hospital (n=10 total) were used for validation in each fold. Accuracy was evaluated using the Dice coefficient [12] and average symmetric surface distance (ASD) [13]. Furthermore, to determine the effect of segmentation method differences, absolute differences of radiodensity values were compared between manual segmentation and automated segmentation regions.

Calculation of density using the regression models
To analyze the effect of calibration, the linear regression models were applied to the range between −100 and 700 HU (the radiodensity range of human tissues often used as the target of clinical analysis), and the results were compared between hospitals.

Comparison between the conventional manual method and the automated method
The regression models of the automated method were compared with regression models with the conventional manual method. Specifically, circular-shaped regions of interest were manually defined for each rod on three axial CT slices, and a linear regression model was generated using the mean values for the three slices (Fig. 3). Correlation coefficients were compared between the conventional manual method and the automated method.

Statistical analysis
Normality was assessed with a Shapiro-Wilk test; data were expressed as mean ± standard deviation when normally distributed and as median (interquartile range) when no t normally distributed. Data were compared using the Mann-Whitney U-test when not normally distributed. The Benjamini-Hochberg procedure was used to correct for multiple comparisons. To compare paired non-normally distributed data, the Wilcoxon signed-rank test was used. All statistical analyses were performed using Matla b and values of p<0.05 were considered statistically significant.

Segmentation accuracy
Automated segmentation was possible in all 1000 test cases including challenging cases with severe artifacts as a result of metal implants in the field of view and cases with the phantom partially located outside of the field of view (Fig. 4). The source code and the model used for segmenting the phantom are open and can be accessed via https://github.com/keisuke-uemura/CT-Intensity-Calibration-Phantom-Segmentation.
After 4-fold cross validation, the median Dice coefficient was 0.977 (0.023), and the median ASD was 0.116 mm (0.108) (Fig. 5). No significant difference between hospitals was found for the Dice coefficient (p=0.84), but the ASD for hospital A was significantly larger than that for hospital B (p=0.02) ( Table 2). When segmented regions were compared between manual segmentation and automated segmentation, the median absolute difference was 0.142 HU (0.280) for hospital A and 0.082 HU (0.124) for hospital B, an overall absolute difference of 0.114 HU (0.158) ( Table 2).

Comparison between hospital regression models
The median correlation coefficient of the regression was 0.9998 (0.0004) for hospital A and 0.9999 (0.0001) for hospital B. The median slope of the regression model was 0.841 (0.027) for hospital A and 0.744 (0.041) for hospital B (Fig. 6), which was significantly different (p<0.001). For the range between −100 and 700 HU, there were significant differences between the hospitals for the ranges between −100 and 4 HU and between 11 and 700 HU-the difference in the result for tissue density was 0.6 mg/cm 3 for 0 HU, which expanded to 58.2 mg/cm 3 for 600 HU (Fig. 4).

Comparison between the conventional manual method and the automated method
The median correlation coefficient of the regression models was 0.9999 (0.0002) for the conventional manual method and 0.9999 (0.0001) for the automated method. No significant difference was found between the methods (p=0.12).

Discussion
We applied a convolutional neural network to automatically segment the corresponding regions of differing radiodensity that corresponded to the different known tissue densities of an intensity calibration phantom used in clinical CT images. The model accuracy-Dice coefficient (0.977) and ASD (0.116 mm)-and robustness-the overall absolute difference between manual and automated segmentations (0.114 HU)-were excellent after training; however, when tissue densities were calculated, significant differences were found between the two hospitals, especially for larger values in the typical clinical range.
In quantitative CT analyses, researchers typically select the calibration phantom manually from a few axial slices of the CT images to create a regression model between radiodensity and tissue density. When the correlation coefficients of the regression models were compared between the conventional manual method and the automated method, no significant difference was found. Importantly, the median correlation coefficient s of both methods were higher than 0.999. Thus, the automated method is at least equivalent to the conventional manual method for developing calibration regression models. Since the automated method does not require a manual process (selecting axial slices, selecting regions of interest, and creating regression models), the automated system saves time and suits for multicenter studies where many researchers participate.
There were significant differences between the tissue densities calculated with hospital A's regression model and hospital B's regression model when CT image radiodensity ranged from −100 to 4 and from 11 to 700. Small differences, even if statistically significant, may not be clinically important. However, because of the difference in the slope of the regression models (Fig. 4), the differences in tissue density value increased as the radiodensity value increased-resulting in a difference of 58.2 mg/cm 3 at 600 HU Recently, studies have employed convolutional neural networks to diagnose hip diseases [18,19] and to segment musculoskeletal regions in CT images [11]. To the best of our knowledge, no studies have developed an automated system that uses a convolutional neural network to segment the regions of imaging calibration phantoms in CT images, which makes our study novel and clinically important. In recent studies, a phantom-less calibration method using internal reference tissues of each patient , such as the aortic blood tissue, pelvic visceral adipose tissue, muscle, and fat, have been reported [10,20] with conflicting results-one paper reported the usefulness of the method [10] while the other recommended caution [20]. Importantly, these previous studies included only a limited number of cases because manual selection of the reference and of the regions of interest was necessary. It would be interesting to apply t he system developed in this study to clarify the usefulness/accuracy of the phantom-less calibration method.
It is important to note that manual efforts are still necessary to quantify BMD from CT images because the bone regions of interest must be selected manually. Because this process is time consuming and prone to error [21], in future studies, we aim to explore developing a convolutional neural network to isolate the region of interest (e.g., femoral neck and spine) to create a fully automated system, which would pave the way for conducting multicenter quantitative CT studies with large datasets.

Limitations
There were some limitations in this study. First, while the system was tested at two hospitals with different CT devices, imaging protocols, and reconstruction protocols including two types of convolution kernels (i.e., soft tissue and bone) that are commonly used in the field of orthopedics, results may vary if CT images are acquired in different situations. However, it is likely that, if an appropriate training dataset is added, the system would be able to perform sufficiently. Second, because only axial slices were used, images