Automated segmentation of 3-D body composition on computed tomography
This work addresses the need for efficient and accurate body composition analysis in medical imaging, which is incremental as it applies existing CNN methods to a specific segmentation task.
The researchers tackled the problem of automatically segmenting five body composition tissues from CT scans using convolutional neural networks, achieving Dice coefficients ranging from 0.603 to 0.908 across different tissues with UNet performing best overall.
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.