Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction

TL;DR

This paper introduces a structure-agnostic vessel segmentation framework that enhances small vessels and corrects morphology, achieving superior accuracy and connectivity across multi-modality datasets, with potential clinical applications.

Contribution

The paper presents a novel, general-purpose vessel segmentation method that improves connectivity and accuracy by combining small vessel enhancement and morphological correction.

Findings

Achieved 34.6% improvement in vessel connectivity.

Outperformed six SAM-based and 17 expert models.

Demonstrated superior generalization across 17 datasets.

Abstract

Accurate segmentation of blood vessels is essential for various clinical assessments and postoperative analyses. However, the inherent challenges of vascular imaging, such as sparsity, fine granularity, low contrast, data distribution variability, and the critical need for preserving topological structure, making generalized vessel segmentation particularly complex. While specialized segmentation methods have been developed for specific anatomical regions, their over-reliance on tailored models hinders broader applicability and generalization. General-purpose segmentation models introduced in medical imaging often fail to address critical vascular characteristics, including the connectivity of segmentation results. To overcome these limitations, we propose an optimized vessel segmentation framework: a structure-agnostic approach incorporating small vessel enhancement and morphological correction for multi-modality vessel segmentation. To train and validate this framework, we compiled a comprehensive multi-modality dataset spanning 17 datasets and benchmarked our model against six SAM-based methods and 17 expert models. The results demonstrate that our approach achieves superior segmentation accuracy, generalization, and a 34.6% improvement in connectivity, underscoring its clinical potential. An ablation study further validates the effectiveness of the proposed improvements. We will release the code and dataset at github following the publication of this work.