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Retinal blood vessel segmentation by using the MS-LSDNet network and geometric skeleton reconnection method.

Hongwei Du1, Xinyue Zhang1, Gang Song1

  • 1School of Mathematics, Shandong University, Jinan, Shandong 250100, China.

Computers in Biology and Medicine
|December 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel three-stage strategy for retinal blood vessel segmentation, enhancing deep learning methods. The approach effectively maintains vascular tree connectivity, crucial for diagnosing eye diseases.

Keywords:
Deep learningFundus imagesVessel reconnectionVessel segmentation

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate retinal blood vessel segmentation is vital for diagnosing ophthalmic diseases.
  • Deep learning models achieve high segmentation accuracy but struggle with vascular structural connectivity.
  • Maintaining vessel connectivity is essential for reliable diagnostic interpretation.

Purpose of the Study:

  • To propose a novel, multi-stage strategy for retinal blood vessel segmentation.
  • To address the challenge of maintaining vascular structural connectivity in automated segmentation.
  • To improve the detection and reconnection of fine and broken blood vessels.

Main Methods:

  • A multiscale linear structure detection network (MS-LSDNet) was developed for enhanced fine vessel detection.
  • An adaptive hysteresis thresholding method was employed for robust vascular extraction and binarization.
  • A geometric skeleton-based algorithm was utilized for reconstructing and reconnecting broken vessel segments.

Main Results:

  • The proposed strategy demonstrated superior performance in maintaining retinal vascular tree connectivity compared to state-of-the-art methods.
  • Experimental results on three public datasets validated the effectiveness of the three-stage approach.
  • The MS-LSDNet improved the detection of hierarchical features, aiding in fine vessel identification.

Conclusions:

  • The novel three-stage retinal blood vessel segmentation strategy effectively preserves vascular structural connectivity.
  • This method offers a significant advancement for accurate ophthalmic disease diagnosis.
  • The integrated approach of detection, extraction, and reconnection provides a robust solution for vessel segmentation challenges.