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Related Experiment Video

Updated: Jul 14, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep-learning segmentation method for optical coherence tomography angiography in ophthalmology.

Fei Ma1, Sien Li1, Shengbo Wang1

  • 1School of Computer Science, Qufu Normal University, Shandong, China.

Journal of Biophotonics
|October 6, 2023
PubMed
Summary

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Accurate segmentation of the optic disc and macula is crucial for detecting eye diseases. A new network, CFANet, demonstrates high performance in segmenting these structures in OCTA images, aiding automated screening.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • The optic disc and macula are key anatomical structures in the human eye.
  • Segmentation of these regions is vital for automated screening of retinal diseases.
  • Degeneration and dysfunction of the macula can lead to vision impairment.

Purpose of the Study:

  • To develop a reliable method for segmenting the optic disc and macula in optical coherence tomography angiography (OCTA) images.
  • To facilitate automated screening of retinal diseases.

Main Methods:

  • A swept-source OCTA system was utilized to capture high-resolution images.
  • A new dataset, the Optic Disc and Macula in fundus Image with OCTA (ODMI) dataset, was constructed.
  • A Coarse and Fine Attention-Based Network (CFANet) was proposed for segmentation.
Keywords:
OCTA fundus databasedeep learning networkmedical image segmentation

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Main Results:

  • The proposed CFANet achieved high performance on the ODMI dataset.
  • Specific performance metrics included 98.91%, 98.47%, 89.77%, 98.49%, and 89.77% for the segmentation tasks.
  • These results indicate robust segmentation capabilities.

Conclusions:

  • The CFANet demonstrates excellent performance for optic disc and macula segmentation in OCTA.
  • The developed method shows promise for improving automated retinal disease screening.
  • Accurate segmentation is a critical step in diagnosing and monitoring eye conditions.