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Automatic Segment and Quantify Choroid Layer in Myopic eyes: Deep Learning based Model.

Chung-Hao Hsiao1, Yu-Len Huang2, Siu-Lun Tse2

  • 1Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan.

Seminars in Ophthalmology
|February 9, 2022
PubMed
Summary

Deep learning accurately measures choroidal thickness (CT) in myopic eyes, revealing CT decreases with increased refractive error (RE). This automated method offers a rapid, clinical alternative to manual measurements.

Keywords:
Deep-learningMask R-CNNchoroidal thicknessoptical coherence tomographyrefractive error

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Choroidal thickness (CT) is a key indicator in myopia research.
  • Accurate CT measurement is crucial for understanding refractive error (RE) progression.
  • Manual segmentation of CT from Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) is time-consuming and subjective.

Purpose of the Study:

  • To develop and validate a deep learning model for automated CT segmentation and measurement.
  • To assess the relationship between refractive error (RE) and CT in myopic individuals.
  • To establish a rapid and accurate method for CT analysis in clinical settings.

Main Methods:

  • A Mask Region-convolutional Neural Network (Mask R-CNN) model with ResNet and Feature Pyramid Networks was trained for automated choroidal layer segmentation.
  • Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) data from 54 healthy subjects (20-39 years) were analyzed.
  • Deep learning-derived CT measurements were compared with manual segmentation and correlated with refractive error and demographic data.

Main Results:

  • The deep learning model achieved 90% accuracy with an average execution time of 6.97 seconds.
  • CT measured by deep learning (226.39 ± 54.65 µm) showed a positive correlation with RE (r=0.546, p<0.01).
  • Both manual and deep learning methods confirmed that CT decreases with increasing myopic RE and is associated with gender and RE.

Conclusions:

  • The Mask R-CNN deep learning model provides an accurate and rapid method for CT measurement.
  • This automated approach eliminates the need for manual segmentation, offering clinical feasibility.
  • The study confirms the association between refractive error and choroidal thickness, with CT decreasing in myopia.