Automated segmentation of 3-D body composition on computed tomography

• URL: http://arxiv.org/abs/2112.08968v1
• Date: Thu, 16 Dec 2021 15:38:27 GMT
• Title: Automated segmentation of 3-D body composition on computed tomography
• Authors: Lucy Pu, Syed F. Ashraf, Naciye S Gezer, Iclal Ocak, Rajeev Dhupar
• Abstract summary: Five different body compositions were manually annotated (VAT, SAT, IMAT, SM, and bone) Ten-fold cross-validation method was used to develop and validate the performance of several convolutional neural networks (CNNs) Among the three CNN models, UNet demonstrated the best overall performance in jointly segmenting the five body compositions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: To develop and validate a computer tool for automatic and simultaneous segmentation of body composition depicted on computed tomography (CT) scans for the following tissues: visceral adipose (VAT), subcutaneous adipose (SAT), intermuscular adipose (IMAT), skeletal muscle (SM), and bone. Approach: A cohort of 100 CT scans acquired from The Cancer Imaging Archive (TCIA) was used - 50 whole-body positron emission tomography (PET)-CTs, 25 chest, and 25 abdominal. Five different body compositions were manually annotated (VAT, SAT, IMAT, SM, and bone). A training-while-annotating strategy was used for efficiency. The UNet model was trained using the already annotated cases. Then, this model was used to enable semi-automatic annotation for the remaining cases. The 10-fold cross-validation method was used to develop and validate the performance of several convolutional neural networks (CNNs), including UNet, Recurrent Residual UNet (R2Unet), and UNet++. A 3-D patch sampling operation was used when training the CNN models. The separately trained CNN models were tested to see if they could achieve a better performance than segmenting them jointly. Paired-samples t-test was used to test for statistical significance. Results: Among the three CNN models, UNet demonstrated the best overall performance in jointly segmenting the five body compositions with a Dice coefficient of 0.840+/-0.091, 0.908+/-0.067, 0.603+/-0.084, 0.889+/-0.027, and 0.884+/-0.031, and a Jaccard index of 0.734+/-0.119, 0.837+/-0.096, 0.437+/-0.082, 0.800+/-0.042, 0.793+/-0.049, respectively for VAT, SAT, IMAT, SM, and bone. Conclusion: There were no significant differences among the CNN models in segmenting body composition, but jointly segmenting body compositions achieved a better performance than segmenting them separately.

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