Fully automated deep-learning section-based muscle segmentation from CT images for sarcopenia assessment

Abstract

Aim: To develop a fully automated deep-learning-based approach to measure muscle area for assessing sarcopenia on standard-of-care computed tomography (CT) of the abdomen without any case exclusion criteria, for opportunistic screening for frailty.

Materials and methods: This ethically approved retrospective study used publicly available and institutional unselected abdominal CT images (n=1,070 training, n=31 testing). The method consisted of two sequential steps: section detection from CT volume followed by muscle segmentation on single-section. Both stages used fully convolutional neural networks (FCNN), based on a UNet-like architecture. Input data consisted of CT volumes with a variety of fields of view, section thicknesses, occlusions, artefacts, and anatomical variations. Output consisted of segmented muscle area on a CT section at the L3 vertebral level. The muscle was segmented into erector spinae, psoas, and rectus abdominus muscle groups. Output was tested against expert manual segmentation.

Results: Threefold cross-validation was used to evaluate the model. Section detection cross-validation error was 1.41 ± 5.02 (in sections). Segmentation cross-validation Dice overlaps were 0.97 ± 0.02, 0.95 ± 0.04, and 0.94 ± 0.04 for erector spinae, psoas, and rectus abdominus, respectively, and 0.96 ± 0.02 for the combined muscle area, with R² = 0.95/0.98 for muscle attenuation/area in 28/31 hold-out test cases. No statistical difference was found between the automated output and a second annotator. Fully automated processing took <1 second per CT examination.

Conclusions: A FCNN pipeline accurately and efficiently automates muscle segmentation at the L3 vertebral level from unselected abdominal CT volumes, with no manual processing step. This approach is promising as a generalisable tool for opportunistic screening for frailty on standard-of-care CT.