MOSBNet: Multi-Object Segmentation in Brachial Plexus Ultrasound

Multi-structure segmentation in brachial plexus ultrasound requires delineating small nerves alongside larger muscles and vessels despite ambiguous tissue boundaries. We propose MOSBNet, which combines category-conditioned feature processing, multi-scale fusion and auxiliary point supervision. The Target Equalized Attention Module (TEAM) uses coarse class masks for branch-specific refinement and region normalization. Upsampling Multi-Scale Fusion (UMF) aggregates decoder features, and Category-Supervised Point Reclassification (CPR) provides training-only supervision at uncertain locations. On a 200-image exploratory evaluation subset of the UBPD 2023 dataset, MOSBNet achieves an mDSC of 73.18 ± 0.69% and nerve DSC of 52.87 ± 0.54% across three training runs compared with the baseline nerve DSC of 50.35 ± 2.48%. Patient-level analysis yields a mean nerve DSC gain of 2.10 percentage points (95% confidence interval 0.87–3.45). Adding TEAM to UMF + CPR improves nerve DSC in all three runs, with a paired gain of 2.34 ± 0.92 percentage points, suggesting a contribution of TEAM within the evaluated configuration.