FSVS-Net: A few-shot semi-supervised vessel segmentation network for multiple organs based on feature distillation and bidirectional weighted fusion

Abstract

Accurate 3D vessel mapping is essential for surgical planning and interventional treatments. However, the conventional manual slice-by-slice annotation in CT scans is extremely time-consuming, due to the complexity of vessels: sparse distribution, intricate 3D topology, varying sizes, irregular shapes, and low contrast with the background. To address this problem, we propose a few-shot semi-supervised vessel segmentation network (FSVS-Net) applicable to multiple organs. It can leverage a few annotated slices to segment vessel regions in unannotated slices, enabling efficient semi-supervised processing of the entire CT sequences. Specifically, we propose a feature distillation module for FSVS-Net to enhance vessel-specific semantic representations and suppress irrelevant background features. In addition, we design a bidirectional weighted fusion strategy that propagates information from a few annotated slices to unannotated ones in both opposite directions of the CT sequence, effectively modeling 3D vessel continuity and reducing error accumulation. Extensive experiments on three datasets (hepatic vessels, pulmonary vessels, and renal arteries) demonstrate that FSVS-Net achieves state-of-the-art performance in few-shot vessel segmentation task, significantly outperforming existing methods. We collected and annotated three vessel datasets, including clinical data from Tsinghua Changgung Hospital and public sources (e.g., MSD08), for this study. In practice, it reduces the average annotation time from 2 h to 0.5 h per volume, improving efficiency by 4$\times$. We release three organ-specific vessel datasets and the implementation code at: https://github.com/YqunYang/FSVS-Net.