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An Open-Source Deep Learning Framework for Automated Corneal Segmentation in Anterior Segment Optical Coherence

Lynn Kandakji1,2, Siyin Liu1,2, Shafi Balal1,2

  • 1University College London, Institute of Ophthalmology, London, United Kingdom; and.

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|April 10, 2026
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Summary
This summary is machine-generated.

The Cornea nnU-Net Extractor (CUNEX) provides accurate full-thickness corneal segmentation from anterior segment optical coherence tomography (AS-OCT) images across multiple devices. This open-source deep learning model offers a robust foundation for ophthalmic artificial intelligence research.

Keywords:
Fuchs endothelial corneal dystrophyU-netanterior segment optical coherence tomographyclassificationdeep learningexternal validationinfectious keratitiskeratoconussegmentation

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate corneal segmentation is crucial for diagnosing and monitoring eye diseases using anterior segment optical coherence tomography (AS-OCT).
  • Existing segmentation models often struggle with performance variability across different OCT devices.

Purpose of the Study:

  • To develop and evaluate the Cornea nnU-Net Extractor (CUNEX), a deep learning model for full-thickness corneal segmentation in AS-OCT images.
  • To assess CUNEX's utility and generalizability in artificial intelligence (AI) research across diverse OCT platforms.

Main Methods:

  • CUNEX was trained on a large AS-OCT dataset and validated internally and externally on images from four different OCT devices.
  • The model's performance was benchmarked against established models (CorneaNet, ScLNet).
  • The impact of CUNEX-based segmentation on downstream AI classification tasks (age, sex, disease staging) was evaluated.

Main Results:

  • CUNEX achieved high segmentation accuracy (Dice Similarity Coefficient [DSC] 94-95%, Intersection over Union [IoU] 88-91%) on internal validation.
  • The model demonstrated superior cross-device generalizability, maintaining meaningful segmentations (DSC/IoU >70%) where other models failed.
  • Segmentation minimally impacted classification accuracy, except for sex prediction, suggesting sex-related features are extracameral.

Conclusions:

  • CUNEX is the first open-source AS-OCT corneal segmentation model validated across multiple independent OCT platforms.
  • It provides a reproducible and robust foundation for integrating corneal segmentation into clinical and AI research.
  • The availability of code and pretrained weights facilitates its adoption in the research community.