DBA-Net: Dynamic Boundary-Aware Network for 3D Medical Point Cloud Segmentation

Abstract

Medical point cloud segmentation accuracy is often limited by feature confusion in boundary regions, which arises from point sparsity, shape complexity, and structural similarity. To address this, we introduce a boundary-aware perspective that categorizes boundaries into inner and outer types. We propose the Dynamic Boundary-Aware Network (DBA-Net), which employs a Boundary-aware Dual Stream (BDS) module to decouple semantic and boundary features via a Cross-Stream Attention Module (CSAM), alongside an Adaptive Boundary Pseudo-Label Calculation (ABP-LC) strategy for adaptive label generation. For outer boundaries, the Outer Boundary Adaptive Contextual Discrepancy-guided Graph Convolution (OACD-GC) module, incorporating a Soft Edge Connection (SEC) strategy and an Iterative Optimization Mechanism (IOM), enhances inter-class discrimination. For inner boundaries, the Inner Boundary Dy-namic Supervised Contrastive Enhancement (ID-SCE) module, utilizing a Multi-Positive Sample (MPS) strategy and a Dynamic Hard Negative Sample Update (DHNU) mechanism, improves intra-class aggregation and inter-class differentiation. Extensive experiments on the IntrA and 3DTeethSeg datasets demonstrate DBA-Net's superior performance in boundary recognition and overall segmentation accuracy.

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