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  1. Home
  3. Machine-learning-based Automated Schlemm's Canal Volumetric Segmentation For Optical Coherence Tomography.
  1. Home
  3. Machine-learning-based Automated Schlemm's Canal Volumetric Segmentation For Optical Coherence Tomography.

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Machine-Learning-Based Automated Schlemm's Canal Volumetric Segmentation for Optical Coherence Tomography.

Raymond Fang1,2, Fengyuanshan Xu1, Zihang Yan1

  • 1Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States.

Chemical & Biomedical Imaging
|June 1, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed Schlemm

Keywords:
Schlemm’s Canalaqueous outflowimage processingmachine learningoptical coherence tomographyoptical imagingsegmentationvolumetric

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Volumetric segmentation of Schlemm's Canal (SC) in optical coherence tomography (OCT) is crucial for studying glaucoma and trabecular outflow pathways.
  • Current manual segmentation methods are time-consuming, hindering in vivo research.

Purpose of the Study:

  • To develop an automated tool, Schlemm's Canal-Localization and Semantic Segmentation (SC-LSS), for efficient SC segmentation in mouse eyes using visible-light OCT (vis-OCT).
  • To validate the accuracy and utility of SC-LSS in capturing physiological changes in SC size.

Main Methods:

  • Developed SC-LSS, a two-stage automated segmentation tool for SC in vis-OCT images.
  • Trained and validated the model using 324 B-scans from 16 mouse eyes.
  • Evaluated model accuracy against manual expert segmentation and assessed SC size changes under varying intraocular pressure and pilocarpine administration.

    • Main Results:

    • SC-LSS achieved a Dice coefficient of 0.70 ± 0.20, comparable to expert inter-grader variability (0.73 ± 0.18).
    • SC-LSS accurately detected a 51.5% decrease in SC size with increased intraocular pressure (20 mmHg vs. 5 mmHg).
    • SC-LSS identified a 20.1% increase in SC size after pilocarpine administration.

    Conclusions:

    • SC-LSS provides accurate and automated volumetric segmentation of Schlemm's Canal in in vivo mouse vis-OCT images.
    • This tool is expected to significantly accelerate research into the trabecular outflow pathways and glaucoma.
    • SC-LSS demonstrates potential for quantitative analysis of SC dynamics in response to physiological stimuli.