JHN-Seg: Multi-scale vascular segmentation via Joint Hierarchical Morphology learning and noisy label refinement

Abstract

Vascular segmentation is essential for various medical applications, such as computer-aided diagnosis, treatment planning, and surgical interventions. However, current deep learning-based vascular segmentation methods face two significant challenges: the complex morphological diversity of vascular structures, which results in discontinuous segmentation in small-scale vessels and incomplete preservation of topological integrity, and the adverse effects of noisy labels during network optimization. To address these challenges, we propose a Joint Hierarchical Vascular Morphology Learning and Noise Label Refinement method (JHN-Seg). JHN-Seg introduces a Hierarchical Vascular Morphology-Aware Network (HVMA-Net) that integrates a Multi-Scale Local Morphology-Aware (MLMA) module, employing a multi-pattern convolutional strategy to adaptively capture intricate vascular features across scales. A Global Morphology Preserving (GMP) loss function is incorporated into HVMA-Net to enforce the continuity of small-scale vessels and maintain the integrality of global vascular structures. Furthermore, JHN-Seg introduces an Uncertainty-Aware Distillation (UD) strategy, which incorporates an Uncertainty Label Refinement (ULR) Module for uncertainty-guided noisy label correction by leveraging pixel-wise KL divergence and consistency generated by teacher-student framework. Comprehensive experiments on liver vessel datasets demonstrate that JHN-Seg outperforms other state-of-the-art segmentation methods. The framework’s adaptability and performance advancements position it as a transformative solution for vascular segmentation and broad applicability to medical image analysis tasks requiring precise morphological representation and noise-robust learning.