Boundary-aware and discrepancy-guided dynamic pseudo-labeling with consistency learning for semi-supervised 3D TOF-MRA cerebrovascular segmentation

<i>Objective.</i>Cerebrovascular diseases are a major global health challenge due to their high morbidity and mortality rates. Accurate segmentation of cerebrovascular structures in TOF-MRA is crucial for accurate diagnosis and treatment planning. However, it remains difficult due to the variability in vessel morphology and the scarcity of annotations.<i>Approach.</i>In this paper, we propose BDD-CL, a boundary-aware and discrepancy-guided dynamic pseudo-labeling consistency learning framework for semi-supervised 3D TOF-MRA cerebrovascular segmentation. The framework is equipped with three carefully designed modules: (1) a boundary enhancement (BE) module that introduces shape constraints to improve vessel boundary delineation; (2) a shape-aware discrepancy (SAD) module that detects and refines prediction inconsistencies between networks, boosting robustness in regions with complex vessel morphology; and (3) a dynamic pseudo-label selection mechanism that adaptively delegates pseudo-label generation to the better-performing network, mitigating error propagation and improving label efficiency.<i>Main results.</i>Extensive experiments on COSTA and IXI datasets demonstrate that BDD-CL surpasses seven state-of-the-art semi-supervised methods in both quantitative and qualitative evaluations.<i>Significance.</i>These results highlight the framework's potential for label-efficient and reliable cerebrovascular segmentation in clinical practice. The code and model will be made publicly available athttps://github.com/nazikelsayed/Boundary-aware-and-discrepancy-guided-dynamic-pseudo-labeling-with-consistency-learning.