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A Deep Learning-Based Fully Automated Program for Choroidal Structure Analysis Within the Region of Interest in

Meng Xuan1, Wei Wang1, Danli Shi1,2

  • 1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.

Translational Vision Science & Technology
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Summary

A new deep learning program (DCAP) automates choroidal structure analysis from OCT scans, offering faster results than manual methods. This tool aids in assessing choroidal structures and may reduce variations in measurements.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Choroidal structure analysis is crucial for diagnosing and monitoring eye diseases.
  • Manual analysis of optical coherence tomography (OCT) scans is time-consuming and prone to variability.
  • Automated methods are needed to improve efficiency and consistency in choroidal assessment.

Purpose of the Study:

  • To develop and validate a deep learning-based choroidal structure assessment program (DCAP) for automated analysis.
  • To analyze choroidal structures within a 1500-µm region centered on the fovea using OCT B-scans.
  • To compare the performance of DCAP against manual measurements by experienced graders.

Main Methods:

  • Utilized a dataset of 2162 fovea-centered SS-OCT B-scans from 162 myopic children.
  • Employed Medical Transformer network and Small Attention U-Net for automatic segmentation of choroid boundaries and foveal nulla.
  • Applied automatic denoising and binarization for isolating choroidal luminal/stromal areas.
  • Compared DCAP measurements (area, choroidal vascularity index [CVI]) with manual measurements from three graders on 20 OCT images.

Main Results:

  • DCAP achieved excellent agreement with manual measurements for luminal/stromal areas (ICCs > 0.900).
  • Agreement for choroidal vascularity index (CVI) was poorer (ICC = 0.627).
  • DCAP significantly reduced analysis time (1 second per image) compared to manual methods (approx. 400 seconds).
  • DCAP demonstrated superior intersession repeatability.

Conclusions:

  • The developed DCAP program provides a faster and automated approach for choroidal structure analysis.
  • DCAP shows potential to reduce intra- and intergrader variability in OCT-based assessments.
  • While effective for area measurements, further refinement may be needed for precise CVI quantification.