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Hierarchical Multi-Scale Mamba with Tubular Structure-Aware Convolution for Retinal Vessel Segmentation.

Tao Wang1,2, Dongyuan Tian1, Haonan Zhao3

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

HM-Mamba, a new deep learning model, enhances retinal vessel segmentation for disease diagnosis. It accurately identifies blood vessels in retinal images, improving diagnostic capabilities.

Keywords:
Mambaattention mechanismmulti-scale fusionretinal vessel segmentationtubular structure-aware convolution

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Retinal vessel segmentation is vital for diagnosing retinal and cardiovascular diseases.
  • Challenges include illumination variations and noise in fundus images, impacting vessel integrity.
  • Accurate segmentation is foundational for computer-aided diagnostic systems.

Purpose of the Study:

  • To introduce HM-Mamba, a novel hierarchical multi-scale Mamba-based architecture for improved retinal vessel segmentation.
  • To enhance the extraction of local and global vascular features by incorporating tubular structure-aware convolution.
  • To improve robustness and accuracy in segmenting complex vascular structures.

Main Methods:

  • Developed a tubular structure-aware convolution to preserve vessel continuity and integrity.
  • Designed a multi-scale fusion module to aggregate features across varying receptive fields.
  • Integrated multi-branch Fourier transform with Mamba for long-range dependency and multi-frequency information capture.
  • Proposed a hierarchical multi-scale interactive Mamba block for effective multi-scale semantic fusion.

Main Results:

  • HM-Mamba demonstrated superior performance on five benchmark datasets (DRIVE, CHASE_DB1, STARE, IOSTAR, LES-AV).
  • Achieved high Dice coefficients: 0.8327 (DRIVE), 0.8197 (CHASE_DB1), 0.8239 (STARE), 0.8307 (IOSTAR), and 0.8426 (LES-AV).
  • The architecture effectively handles complex spatial patterns and reduces detail loss during downsampling.

Conclusions:

  • HM-Mamba offers a robust and effective solution for retinal vessel segmentation.
  • The proposed methods significantly improve the representation of vascular structures, including fine branches.
  • This advancement holds promise for enhancing the accuracy and reliability of computer-aided diagnostic systems for eye and cardiovascular diseases.