BSM-SegNet: Boundary-Scale Synergistic Segmentation Network for Lumbar Muscle MRI Segmentation in Sarcopenia Assessment

Accurate segmentation of lumbar muscle groups is essential for imaging-based screening and quantitative assessment of sarcopenia. However, automatic lumbar muscle segmentation in magnetic resonance imaging (MRI) remains challenging due to indistinct boundaries, adhesion between adjacent muscles, significant inter-class scale variations, and imaging artifacts. To address these challenges, this paper proposes a boundary–scale synergistic perception network for multi-class muscle segmentation, termed Boundary–Scale Synergistic Segmentation Network (BSM-SegNet). The proposed network integrates boundary-sensitive modeling, multi-scale feature representation, and cross-stage feature selection to enhance segmentation accuracy and robustness. Specifically, a Boundary-Sensitive Residual Module (BSRM) is designed to strengthen feature responses in muscle boundary regions through local feature differencing. A Frequency–Scale Coupling Module (FSCM) is introduced to improve multi-scale structural modeling via multi-scale pooling and channel attention. In addition, a Cross-Stage Gating (CSG) module is employed to suppress redundant features and enhance semantic consistency during encoder–decoder feature fusion. Extensive experiments are conducted on a private lumbar muscle MRI dataset with 562 subjects and an external multi-center public dataset with 290 subjects. Experimental results show that BSM-SegNet achieves an IoU of 86.68% and a Dice score of 92.85% on the private dataset, and an IoU of 86.16% and a Dice score of 92.50% on the external dataset, outperforming several representative segmentation methods. Ablation studies further demonstrate the effectiveness and synergistic benefits of the proposed modules. These results indicate that BSM-SegNet is well suited for accurate lumbar muscle segmentation in sarcopenia assessment.