利用血管模式代码本实现高效3D脑血管分割，减少标注依赖。
VPBSD:Vessel-Pattern-Based Semi-Supervised Distillation for Efficient 3D Microscopic Cerebrovascular Segmentation
- 基于未标注数据构建血管模式代码本，指导知识蒸馏。
- 在真实数据上优于现有方法，显著提升分割效率与精度。
- 适合医学图像分割、小样本学习研究者参考。
3D显微脑血管图像具有高分辨率、数据量大、细节变化复杂等特点，导致高质量、高效的全脑分割极具挑战。本文提出一种新型血管模式基半监督蒸馏框架（VpbSD），在教师模型预训练阶段，从无标签数据中构建捕捉多样化血管结构的血管模式代码本。在知识蒸馏阶段，该代码本促进异构教师模型向学生模型传递丰富知识，同时半监督策略进一步增强学生模型对多样样本的学习能力。在真实数据上的实验结果表明，该框架及其各组件均有效应对显微脑血管分割中的固有挑战，性能优于当前最优方法。
原文摘要 · Abstract (English)
3D microscopic cerebrovascular images are characterized by their high resolution, presenting significant annotation challenges, large data volumes, and intricate variations in detail. Together, these factors make achieving high-quality, efficient whole-brain segmentation particularly demanding. In this paper, we propose a novel Vessel-Pattern-Based Semi-Supervised Distillation pipeline (VpbSD) to address the challenges of 3D microscopic cerebrovascular segmentation. This pipeline initially constructs a vessel-pattern codebook that captures diverse vascular structures from unlabeled data during the teacher model's pretraining phase. In the knowledge distillation stage, the codebook facilitates the transfer of rich knowledge from a heterogeneous teacher model to a student model, while the semi-supervised approach further enhances the student model's exposure to diverse learning samples. Experimental results on real-world data, including comparisons with state-of-the-art methods and ablation studies, demonstrate that our pipeline and its individual components effectively address the challenges inherent in microscopic cerebrovascular segmentation.
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