Physics-based generation of multilayer corneal OCT data via Gaussian modeling and MCML for AI-driven diagnostic and surgical guidance applications

2026-02-02

AI Summary

This work addresses the scarcity of high-quality annotated data that limits deep learning applications in corneal OCT image analysis. To overcome this challenge, the authors propose a physics-based, configurable Monte Carlo simulation framework that, for the first time, integrates Gaussian surface geometry modeling of the five corneal layers with MCML-based light transport simulation. The framework further incorporates a confocal point spread function (PSF) and system sensitivity roll-off model to directly generate high-resolution (1024×1024) synthetic OCT images with pixel-level segmentation labels. Crucially, it enables controlled morphological variations to simulate pathological conditions such as keratoconus. Using this approach, the authors have constructed a dataset of over 10,000 image–label pairs, providing a reproducible and high-fidelity benchmark resource for training and evaluating AI models in corneal OCT analysis.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data generation and simulation
• Security and Privacy: Data transparency and provenance

Abstract

Training deep learning models for corneal optical coherence tomography (OCT) imaging is limited by the availability of large, well-annotated datasets. We present a configurable Monte Carlo simulation framework that generates synthetic corneal B-scan optical OCT images with pixel-level five-layer segmentation labels derived directly from the simulation geometry. A five-layer corneal model with Gaussian surfaces captures curvature and thickness variability in healthy and keratoconic eyes. Each layer is assigned optical properties from the literature and light transport is simulated using Monte Carlo modeling of light transport in multi-layered tissues (MCML), while incorporating system features such as the confocal PSF and sensitivity roll-off. This approach produces over 10,000 high-resolution (1024x1024) image-label pairs and supports customization of geometry, photon count, noise, and system parameters. The resulting dataset enables systematic training, validation, and benchmarking of AI models under controlled, ground-truth conditions, providing a reproducible and scalable resource to support the development of diagnostic and surgical guidance applications in image-guided ophthalmology.

Problem

• corneal OCT
• deep learning
• data scarcity
• image annotation
• AI training

Innovation

• Monte Carlo simulation
• Gaussian surface modeling
• synthetic OCT data
• multilayer corneal model
• MCML