VPBSD:Vessel-Pattern-Based Semi-Supervised Distillation for Efficient 3D Microscopic Cerebrovascular Segmentation

TL;DR

This paper introduces VpbSD, a semi-supervised distillation pipeline utilizing vessel-pattern codebooks to improve 3D cerebrovascular segmentation from high-resolution microscopic images, addressing annotation and data complexity challenges.

Contribution

The paper presents a novel vessel-pattern-based semi-supervised distillation method that leverages unlabeled data for efficient and high-quality 3D cerebrovascular segmentation.

Findings

Outperforms state-of-the-art methods on real-world data

Effective knowledge transfer from heterogeneous teacher models

Ablation studies confirm the importance of each component

Abstract

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.