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

Nazik Mohamad Ahmed Elsayed1,2,3, Jiarun Liu1,2,4, Cheng Li1

  • 1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.

Physics in Medicine and Biology
|January 20, 2026
PubMed

Insights

This study introduces BDD-CL, a novel framework for semi-supervised cerebrovascular segmentation in 3D TOF-MRA scans. The method enhances accuracy and efficiency in segmenting complex vessel structures, aiding clinical diagnosis.

Area of Science:

  • Medical Imaging
  • Neuroscience
  • Computer Vision

Background:

  • Cerebrovascular diseases pose significant global health risks, necessitating accurate diagnostic tools.
  • Precise segmentation of cerebrovascular structures in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) is vital for diagnosis and treatment planning.
  • Current segmentation methods face challenges due to anatomical variability and limited annotated data.

Purpose of the Study:

  • To develop a semi-supervised framework for accurate cerebrovascular segmentation in 3D TOF-MRA.
  • To address limitations in vessel morphology variability and annotation scarcity.
  • To improve the efficiency and reliability of cerebrovascular segmentation for clinical applications.

Main Methods:

  • Proposed BDD-CL (Boundary-Aware and Discrepancy-Guided Dynamic pseudo-labeling Consistency Learning) framework.
  • Incorporated a Boundary Enhancement (BE) module for improved vessel boundary delineation using shape constraints.
  • Integrated a Shape-Aware Discrepancy (SAD) module to enhance robustness in complex morphologies by refining prediction inconsistencies.
  • Utilized a Dynamic Pseudo-label Selection (DPS) mechanism for adaptive pseudo-label generation, optimizing label efficiency and mitigating error propagation.

Main Results:

  • BDD-CL demonstrated superior performance compared to seven state-of-the-art semi-supervised methods.
  • Quantitative and qualitative evaluations on COSTA and IXI datasets confirmed the framework's effectiveness.
  • The method achieved significant improvements in cerebrovascular segmentation accuracy.

Conclusions:

  • The BDD-CL framework shows strong potential for label-efficient and reliable cerebrovascular segmentation in clinical practice.
  • The proposed approach offers a promising solution for overcoming challenges in TOF-MRA segmentation.
  • The study contributes to advancing automated analysis of cerebrovascular structures.

Keywords:
dynamic pseudo-label selectionsemi-supervised learningshape-aware Discrepancy

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