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Interleaving Automatic Segmentation and Expert Opinion for Retinal Conditions.

Sergiu Bilc1, Adrian Groza1, George Muntean2

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Summary

This study introduces a novel graph theory and geodesic distance method for segmenting retina layers in optical coherence tomography (OCT) scans. The tool supports ophthalmologists, enhancing diagnostic accuracy with explainable AI features.

Keywords:
geodesic distancehuman-centered AIoptical coherence tomographyretina layer segmentationvertical and horizontal gradients

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Optical coherence tomography (OCT) is a key diagnostic technology in ophthalmology.
  • Accurate segmentation of retinal layers is crucial for diagnosing eye diseases.
  • Existing segmentation methods may lack precision or transparency.

Purpose of the Study:

  • To develop an AI-powered support tool for automated retinal layer segmentation using OCT images.
  • To improve the accuracy and reliability of retinal layer segmentation.
  • To integrate explainable AI (XAI) principles for enhanced transparency and control.

Main Methods:

  • The proposed method utilizes graph theory and geodesic distance for segmentation.
  • It incorporates various gradients (horizontal, vertical, open-closed) to characterize different retinal layers.
  • The approach allows for human intervention, including validation and fine-tuning by ophthalmologists.

Main Results:

  • The method was evaluated on 750 Spectralis OCT B-Scans.
  • It demonstrated a small signed error on specific layers (B1, B7, B8), with a maximum of 0.43 pixels.
  • The average signed error across all layers was -1.99 ± 1.14 pixels, and the mean absolute error was 2.60 ± 0.95 pixels.

Conclusions:

  • The developed tool offers a promising approach for supporting retinal layer segmentation in OCT.
  • Its human-centered AI design enhances control, transparency, and provides a global perspective on segmentation.
  • This method has the potential to aid ophthalmologists in more precise and efficient diagnosis.