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MSPDD-net: Mamba semantic perception dual decoding network for retinal image vessel segmentation.

Daxiang Li1, Miao Su2, Ying Liu1

  • 1School of Telecommunication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China; Research Center for Wireless Communication and Information Processing Technology of Shaanxi Province, Xi'an, 710121, China.

Computers in Biology and Medicine
|May 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the Mamba Semantic Perception Dual Decoding Network (MSPDD-Net) to improve retinal image vessel segmentation accuracy. The novel network effectively captures global context and edge features, achieving high segmentation performance on public datasets.

Keywords:
MambaMedical image segmentationRetinal image vesselWavelet edge attention

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Accurate retinal image vessel segmentation is crucial for diagnosing eye diseases.
  • Low-contrast capillaries in retinal images present a significant challenge to existing segmentation methods, leading to reduced accuracy.

Purpose of the Study:

  • To enhance the accuracy of retinal image vessel (RIV) segmentation by modeling global contextual information and mining edge features.
  • To introduce a novel deep learning network, the Mamba Semantic Perception Dual Decoding Network (MSPDD-Net), for improved RIV segmentation.

Main Methods:

  • A dual-encoding path with a Position Sensitive Cross-Layer Interactive (PSCLI) strategy was developed for feature extraction.
  • A Mamba Full-Scale Semantic Perception (M-FSSP) module was integrated into the bottleneck to capture comprehensive semantic features.
  • A dual-decoding path incorporating Full-Scale Semantic Injection (FSSI) and Wavelet Edge Attention (WEA) was designed to refine segmentation and highlight vessel edges.

Main Results:

  • The MSPDD-Net achieved high segmentation accuracies of 97.45% (DRIVE), 97.76% (STARE), and 97.88% (CHASEDB1).
  • The network demonstrated a notable 1.59% improvement on the STARE dataset compared to baseline methods.
  • Experimental results confirm the effectiveness of the proposed network in addressing challenges posed by low-contrast capillaries.

Conclusions:

  • The MSPDD-Net offers an effective solution for accurate retinal image vessel segmentation.
  • The integration of Mamba-based modules and attention mechanisms significantly improves the modeling of contextual and edge information.
  • The proposed network shows great potential for clinical applications in ophthalmology.