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Related Experiment Video

Updated: Nov 20, 2025

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Efficient BFCN for Automatic Retinal Vessel Segmentation.

Yun Jiang1, Falin Wang1, Jing Gao1

  • 1College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China.

Journal of Ophthalmology
|January 25, 2021
PubMed
Summary

This study introduces a novel butterfly fully convolutional neural network (BFCN) for enhanced retinal vessel segmentation. The BFCN method improves accuracy in diagnosing diseases like diabetic retinopathy and hypertension.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Ophthalmology

Background:

  • Retinal vessel segmentation is crucial for diagnosing diabetic retinopathy, hypertension, and cardiovascular diseases.
  • Current deep convolutional neural network (DCNN) methods struggle with limited receptive fields and spatial information, hindering global context capture and lesion identification.
  • This leads to poor segmentation efficiency and difficulty in identifying critical areas.

Purpose of the Study:

  • To develop an improved method for retinal vessel segmentation.
  • To enhance the accuracy and efficiency of identifying retinal vasculature for disease diagnosis.
  • To address limitations in existing DCNN-based segmentation approaches.

Main Methods:

  • Proposed a butterfly fully convolutional neural network (BFCN) architecture.
  • Employed automatic color enhancement (ACE) to improve low contrast in retinal images.
  • Integrated a multiscale information extraction (MSIE) module and a transfer layer (T_Layer) to capture global context and preserve spatial information.
  • Utilized Laplacian sharpening for postprocessing segmentation images.

Main Results:

  • Achieved high accuracy on benchmark datasets: 0.9627 (DRIVE), 0.9735 (STARE), and 0.9688 (CHASE).
  • The BFCN method demonstrated improved capability in capturing global context and detailed vessel structures.
  • ACE and Laplacian sharpening effectively addressed low contrast and enhanced segmentation precision.

Conclusions:

  • The proposed BFCN method significantly improves retinal vessel segmentation accuracy.
  • This approach offers a more effective tool for the early diagnosis of various eye and systemic diseases.
  • The integration of ACE, MSIE, T_Layer, and Laplacian sharpening provides a robust solution for retinal image analysis.