[Site-specific Segmentation of Skeletal Muscles in Body CT Images via Comprehensive Muscular Consideration]

The objective of this study is to achieve site-specific three-dimensional (3D) automatic segmentation of skeletal muscles in body CT images. We aimed to improve recognition accuracy of nine muscle regions: sternocleidomastoid, erector spinae, trapezius, supraspinatus, rectus abdominis, obliques, quadratus lumborum, psoas major, and iliacus. Then, we focused on utilizing all skeletal muscle areas outside the target recognition regions that were not previously used.

Our method trains the 2D U-Net to learn both the target site-specific skeletal muscle region and all other skeletal muscles together. We utilized 30 cases of unenhanced body CT images and performed three-fold cross-validation.

The proposed method achieved an average Dice coefficient of 88.37% across nine regions, showing improvements of 25.78% and 1.86% compared to the individual learning of each region (baseline) and the simultaneous learning of erector spinae (previous method), respectively.

Comprehensive learning of all skeletal muscle regions successfully improved the accuracy of U-Net-based 3D automatic segmentation for site-specific skeletal muscles in body CT. It leads to enhanced, precise body composition analysis for skeletal muscle regions.