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Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: Anterior HR-OCT as a Non-Invasive Tool for Characterizing Ocular Surface Squamous Neoplasia
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Deep Learning-based Quantification of Anterior Segment OCT Parameters.

Zhi Da Soh1,2, Mingrui Tan3, Monisha Esther Nongpiur1,4

  • 1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.

Ophthalmology Science
|October 23, 2023
PubMed
Summary
This summary is machine-generated.

A novel deep learning algorithm automates scleral spur annotation and anterior chamber structure segmentation in OCT scans. This advancement enhances accuracy and efficiency for ocular measurements in both open-angle and angle-closure eyes.

Keywords:
Angle closureAnterior chamber measurementsAnterior segment OCT (ASOCT)Deep learningVisante ASOCT

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Anterior segment optical coherence tomography (ASOCT) is crucial for evaluating ocular structures.
  • Accurate annotation of the scleral spur (SS) and segmentation of anterior chamber (AC) structures are vital for glaucoma diagnosis and management.
  • Manual segmentation is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To develop and validate a deep learning algorithm for automated SS annotation and AC structure segmentation in ASOCT scans.
  • To enable precise measurements of AC, iris, and angle width parameters.
  • To reduce subjectivity and improve efficiency in ASOCT-based ocular measurements.

Main Methods:

  • Image contrast enhancement using CycleGAN.
  • A heat map regression approach with a coarse-to-fine framework for SS annotation.
  • An ensemble network (U-Net, full resolution residual network, full resolution U-Net) for structure segmentation.
  • Comparison of algorithm-derived measurements with manual annotations (ground truth).

Main Results:

  • The algorithm achieved high accuracy in SS annotation (Euclidean distance: 124.7 μm, ICC ≥ 0.95, error rate: 3.3%).
  • Excellent segmentation performance with Dice similarity coefficient ≥ 0.91 for cornea, iris, and AC.
  • Angle width measurements showed strong agreement with manual methods (≥ 95% within limits-of-agreement, ICC 0.71-0.87).
  • Algorithm measurements demonstrated less variability compared to a semi-automated program.

Conclusions:

  • A validated deep learning algorithm effectively automates SS annotation and AC structure segmentation in ASOCT scans.
  • The algorithm performs comparably to human experts in both open-angle and angle-closure eyes.
  • This technology significantly reduces measurement time and subjectivity, aiding clinical practice.