TCB‐Net: Topology‐Constrained Boundary‐Interior Decoupled Network for Breast Ultrasound Lesion Segmentation

ABSTRACT

Accurate breast lesion segmentation in ultrasound (BUS) remains challenging due to low contrast and high variability. Current methods focus primarily on feature extraction but often fail to model the topological structure, leading to spurious disconnected predictions and inconsistent boundaries. We propose TCB‐Net, a novel architecture designed to ensure topological and geometric consistency through three key contributions. First, the lesion‐conditioned boundary attention gate (LCBAG) implements a coarse‐to‐fine feedback loop, suppressing background noise using predicted mask priors. Second, the boundary‐interior decoupled decoder (BIDD) utilises morphologically derived supervision to separate interior and boundary learning into distinct gradient signals. Third, the cross‐prediction geometric consistency loss (CPGCL) enforces spatial gradient identity between predictions and incorporates a differentiable soft Euler‐number regulariser to penalise topological errors. Evaluated on BUSI, UDIAT and BUS‐BRA datasets, TCB‐Net achieves Dice scores of 81.97%, 89.06% and 80.58% and HD95 of 12.67, 9.34 and 15.23 pixels, respectively. Notably, boundary‐sensitive metrics show even larger relative gains than Dice: TCB‐Net reduces HD95 by 15.1% relative to the strongest baseline (UMA‐Net) on BUSI, indicating that the proposed topology‐constrained design yields disproportionately large improvements in clinically relevant boundary precision. It outperforms seven state‐of‐the‐art baselines, including Attention U‐Net and HAU‐Net. Ablation studies confirm that our combined components yield a +7.87% Dice improvement over a ResNet34 U‐Net baseline on BUSI, demonstrating its effectiveness in producing clinically reliable, topologically sound segmentations.