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

arXiv: https://arxiv.org/abs/2602.02755 · HTML: https://mlsanity.com/paper/2602.02755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [The Neural Networks Based Needle Detection for Medical Retinal Surgery](https://mlsanity.com/paper/2302.05034/) (2023)
- [Reimagining partial thickness keratoplasty: An eye mountable robot for autonomous big bubble needle insertion](https://mlsanity.com/paper/2410.14577/) (2024)
- [Assessment of Generative Adversarial Networks Model for Synthetic Optical Coherence Tomography Images of Retinal Disorders](https://mlsanity.com/paper/1910.09748/) (2019)
- [Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data](https://mlsanity.com/paper/2501.04735/) (2025)
- [Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors](https://mlsanity.com/paper/2505.14916/) (2025)
- [PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Prior](https://mlsanity.com/paper/2512.18367/) (2025)
- [Corneal Pachymetry by AS-OCT after Descemet's Membrane Endothelial Keratoplasty](https://mlsanity.com/paper/2102.07846/) (2021)

## Abstract (arXiv)

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.