Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning

Purpose: To develop and evaluate a fully automated algorithm for segmenting the abdomen from CT to quantify body composition. Materials and Methods: For this retrospective study, a convolutional neural network based on the U-Net architecture was trained to perform abdominal segmentation on a data set of 2430 two-dimensional CT examinations and was tested on 270 CT examinations. It was further tested on a separate data set of 2369 patients with hepatocellular carcinoma (HCC). CT examinations were performed between 1997 and 2015. The mean age of patients was 67 years; for male patients, it was 67 years (range, 2994 years), and for female patients, it was 66 years (range, 3197 years). Differences in segmentation performance were assessed by using two-way analysis of variance with Bonferroni correction. Results: Compared with reference segmentation, the model for this study achieved Dice scores (mean +/- standard deviation) of 0.98 +/- 0.03, 0.96 +/- 0.02, and 0.97 +/- 0.01 in the test set, and 0.94 +/- 0.05, 0.92 +/- 0.04, and 0.98 +/- 0.02 in the HCC data set, for the subcutaneous, muscle, and visceral adipose tissue compartments, respectively. Performance met or exceeded that of expert manual segmentation. Conclusion: Model performance met or exceeded the accuracy of expert manual segmentation of CT examinations for both the test data set and the hepatocellular carcinoma data set. The model generalized well to multiple levels of the abdomen and may be capable of fully automated quantification of body composition metrics in three-dimensional CT examinations. (c) RSNA, 2018