The amount and anatomical distribution of fat and muscle in different body compartments is an important prognostic factor in patients with cardiovascular disease. Although this information is routinely contained in many types of CT scans it is hard to quantify in daily clinical routine because manual segmentation is time consuming, especially in 3D. The purpose of this study is to investigate the use of a deep learning-based method for automatic segmentation of subcutaneous fat, visceral fat and psoas muscle.
Methods and Materials
The feasibility of fully automated body composition measurement was studied using a dataset of 20 CT scans of the abdomen (in-plane resolution 0.63–0.75 mm, slice thickness 5.0 mm, slice increment 5.0 mm). Trained observers defined the reference standard by manual annotation of subcutaneous fat, visceral fat and psoas muscle in all slices that contain the psoas muscle. Images from 10 patients were used to train a dilated convolutional neural network with a receptive field of 131 × 131 voxels to distinguish between the three tissue...
On average, segmentation of a full scan was performed in about 15 seconds. The average Dice coefficients over 10 test scans were 0.89 ± 0.022 for subcutaneous fat, 0.92 ± 0.042 for visceral fat, and 0.76 ± 0.052 for psoas muscle. At the L3 vertebrae level, the average Dice coefficients were 0.92 ± 0.019 for subcutaneous fat, 0.93 ± 0.048 for visceral fat, and 0.87 ± 0.035 for psoas muscle. Pearson’s r between the manual and automatic volumes were 0.996 for subcutaneous fat, 0.997 for...
The results show that accurate fully automatic segmentation of subcutaneous fat, visceral fat and psoas muscle from abdominal CT is feasible without human input.