Abstract
Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation of body composition.Methods For model development, CT images of 100 patients who underwent a whole-body or torso 18F-fluorodeoxyglucose PET–CT scan were retrospectively included. Two radiologists semi-automatically labeled the following seven body components in every CT image slice, providing 39,268 images for training the 3D U-Net: skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs with vessels, and central nervous system. The segmentation accuracy was assessed using reference masks from one internal and three external datasets: two domestic centers (n=20, each) and a French public dataset (n=24). The 3D U-Net-driven values were clinically validated using bioelectrical impedance analysis (BIA) and by assessing the model’s diagnostic performance for sarcopenia in a community-based elderly cohort (n=522).Results The 3D U-Net achieved accurate body composition segmentation with an average dice similarity coefficient of 96.5% to 98.9% for all masks and 92.3% to 99.3% for muscle, abdominal visceral fat, and subcutaneous fat in the validation datasets. The 3D U-Net-derived torso volume of skeletal muscle and fat tissue and the average area of those tissues in the waist were correlated with BIA-derived appendicular lean mass (correlation coefficients: 0.71 and 0.72, each) and fat mass (correlation coefficients: 0.95 and 0.93, each). The 3D U-Net-derived average areas of skeletal muscle and fat tissue in the waist were independently associated with sarcopenia (P<.001, each) with adjustment for age and sex, providing an area under the curve of 0.858 (95% CI, 0.815 to 0.901).Conclusions This deep neural network model enabled the automatic volumetric segmentation of body composition on whole-body CT images, potentially expanding adjunctive sarcopenia assessment on PET-CT scan and volumetric assessment of metabolism in whole-body muscle and fat tissues.
Surface Muscle Segmentation Using 3D U-Net Based on Selective Voxel Patch Generation in Whole-Body CT Images