用蒙特卡洛模拟生成带分层标签的角膜OCT图像，解决数据不足问题。
Physics-based generation of multilayer corneal OCT data via Gaussian modeling and MCML for AI-driven diagnostic and surgical guidance applications
- 基于高斯曲面和蒙特卡洛光传输模拟，生成五层角膜结构。
- 产生成像对超1万组，分辨率1024x1024，含像素级标签。
- 适用于眼科诊断与手术导航的AI模型训练与验证。
训练用于角膜光学相干断层扫描（OCT）成像的深度学习模型受限于大规模、高质量标注数据集的可用性。本文提出一种可配置的蒙特卡洛仿真框架，直接从仿真几何结构生成带有像素级五层分割标签的合成角膜B-scan OCT图像。采用五层角膜模型，其表面由高斯函数描述，以捕捉健康与圆锥角膜眼的曲率和厚度变化。各层赋予文献中的光学属性，通过多层组织光传输蒙特卡洛建模（MCML）模拟光传播，并融合共焦点扩散函数（PSF）和灵敏度滚降等系统特性。该方法生成超过10,000组高分辨率（1024x1024）图像-标签对，支持几何、光子数、噪声及系统参数的定制。所获数据集可用于在受控、真实标签条件下系统性训练、验证和基准测试AI模型，为影像引导眼科诊断与手术导航应用提供可复现、可扩展的资源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。