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

TL;DR

This paper introduces a Monte Carlo simulation framework that generates realistic synthetic corneal OCT images with detailed labels, aiding AI model training and validation in ophthalmology.

Contribution

A novel, configurable simulation method producing large, labeled OCT datasets with customizable parameters for improved AI development in corneal imaging.

Findings

Generated over 10,000 high-resolution synthetic OCT images.

Enabled systematic training and benchmarking of AI models.

Provided a reproducible, scalable dataset for ophthalmic AI applications.

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.