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Updated: Aug 15, 2025

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A deep network embedded with rough fuzzy discretization for OCT fundus image segmentation.

Qiong Chen1,2, Lirong Zeng3, Cong Lin4,5

  • 1College of Electronic and Information Engineering, Guangdong Ocean University, Haida Road, Zhanjiang, 524000, Guangdong, China. 13907534385@163.com.

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|January 7, 2023
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Summary

This study introduces a novel deep network for Optical Coherence Tomography (OCT) fundus image segmentation, significantly reducing noise and improving accuracy. The Rough Fuzzy Discretization Network (RFDDN) outperforms existing methods in key segmentation metrics.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Deep learning algorithms for medical image segmentation face performance bottlenecks due to noise and redundant information.
  • Accurate segmentation of Optical Coherence Tomography (OCT) fundus images is crucial for diagnosing retinal diseases.

Purpose of the Study:

  • To propose a novel deep network, Rough Fuzzy Discretization Network (RFDDN), for enhanced OCT fundus image segmentation.
  • To address noise and redundant information issues in deep learning-based segmentation.

Main Methods:

  • Developed RFDDN integrating rough fuzzy discretization for feature preprocessing.
  • Utilized fuzzy c-means clustering and a genetic algorithm for optimal feature discretization.
  • Incorporated a deep supervised attention mechanism for multi-scale information extraction.

Main Results:

  • RFDDN demonstrated superior performance across all evaluation indicators compared to U-Net, ReLayNet, CE-Net, MultiResUNet, and ISCLNet.
  • RFDDN achieved higher Dice Similarity Coefficient (DSC), sensitivity, and specificity than ISCLNet.
  • RFDDN showed lower Hausdorff Distance 95 (HD95) and Average Surface Distance (ASD) than ISCLNet.

Conclusions:

  • The proposed RFDDN effectively eliminates noise and redundant information in OCT fundus images.
  • RFDDN significantly improves the accuracy of OCT fundus image segmentation.
  • The method balances interpretability and computational efficiency, offering a robust solution for retinal image analysis.