A new approach to optical coherence tomography image analysis using machine learning

使用机器学习进行光学相干断层扫描图像分析的新方法

• 批准号: BB/P027105/1
• 负责人: Li Bai
• 金额: $ 18.94万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP027105%2F1
• 关键词: new; approach; optical; coherence; tomography

This proposal describes a novel and adventurous pump-priming (pilot) project. The project fits the BBSRC Call for developing "new approaches to the analysis and interpretation of research data in bioimaging, including the development of software tools and algorithms that address challenges arising from emerging new types of data and known problems associated with data handling". Specifically, in this pilot project we will develop a revolutionary approach to analyzing optical coherence tomography (OCT) images, that is, to automatically extract properties that may not be visible to the eye from the images using a mathematical technique called inversion, as well as machine learning techniques and knowledge about the physics of OCT imaging. OCT is a relatively new imaging modality, which provides non-invasive imaging of living tissues based on the principle of optical interferometry. Contrast in OCT is derived from the difference in light scattering and absorption properties of tissue structures. As optical scattering is more varied across soft tissues than either acoustic scattering or x-ray absorption, thus OCT generally provides greater contrast than computed tomography (CT) and ultrasound and can even be sensitive to tissue structural properties at the nanometer length scale. Recent advances in OCT have mainly been driven by applications in biomedical applications such as ophthalmology to study optical nerves development and diagnose glaucoma. These applications rely on the fact that most physiological changes or disease processes affect the optical properties of biological tissue, and this change in tissue optical properties provide the contrast for this imaging technology. Application of OCT is however not limited to the medical domain, e.g., it is also being used in brain imaging, developmental biology, and in tissue engineering. The motivation of the project is the limitation of current approaches to optical coherence tomography (OCT) image analysis, which are subjective and require that the image features to be detected are visible to the eye. However, most often the vital features are invisible to the eye as biological or disease processes are complex and this complexity is reflected in the images. The project will develop a radically different approach to OCT image analysis that extracts tissue optical properties that are invisible to the eye from OCT images to address life and health science research challenges, where little pilot data exists.

这项建议描述了一个新颖而冒险的泵启动(试验)项目。该项目符合BBSRC的要求，即开发“分析和解释生物成像研究数据的新方法，包括开发软件工具和算法，以应对新出现的新类型数据和与数据处理相关的已知问题带来的挑战”。具体地说，在这个试点项目中，我们将开发一种革命性的方法来分析光学相干层析成像(OCT)图像，即使用一种称为反演的数学技术以及机器学习技术和OCT成像物理知识自动从图像中提取肉眼不可见的属性。OCT是一种相对较新的成像方式，它基于光学干涉原理对活体组织进行非侵入性成像。OCT的对比度来自于组织结构的光散射和吸收特性的差异。由于光散射在软组织中的变化比声波散射或X射线吸收更大，因此OCT通常比计算机断层扫描(CT)和超声波提供更大的对比度，甚至可以对纳米尺度上的组织结构属性敏感。OCT的最新进展主要是由于其在生物医学应用中的应用，如眼科研究视神经发育和诊断青光眼。这些应用依赖于这样一个事实，即大多数生理变化或疾病过程影响生物组织的光学特性，而组织光学特性的这种变化为这种成像技术提供了对比。然而，OCT的应用并不局限于医学领域，例如，它还被用于脑成像、发育生物学和组织工程。该项目的动机是目前光学相干层析(OCT)图像分析方法的局限性，这些方法是主观的，要求被检测的图像特征是肉眼可见的。然而，由于生物或疾病过程很复杂，所以最重要的特征通常是肉眼看不见的，这种复杂性反映在图像中。该项目将开发一种截然不同的OCT图像分析方法，从OCT图像中提取肉眼不可见的组织光学属性，以应对生命和健康科学研究的挑战，在这些领域几乎没有试点数据。

项目成果

Analytical Models of Optical Coherence Tomography for Tissue Optical Property Estimation

用于组织光学特性估计的光学相干断层扫描分析模型

• 发表时间: 2018
• 作者: Wang Y

OCT segmentation: Integrating open parametric contour model of the retinal layers and shape constraint to the Mumford-Shah functional

• DOI: 10.1007/978-3-030-04747-4_17
• 发表时间: 2018-08
• 期刊: ArXiv
• 作者: J. Duan;Weicheng Xie;R. W. Liu;C. Tench;I. Gottlob;F. Proudlock;L. Bai

Comparison of EHF and RTE for OCT Modelling

OCT 建模的 EHF 和 RTE 比较

• 发表时间: 2018
• 期刊: Basic & Clinical Pharmacology & Toxicology
• 影响因子: 3.1
• 作者: Wang L

Accurate Monte Carlo simulation of frequency-domain optical coherence tomography

• DOI: 10.1002/cnm.3177
• 发表时间: 2019-04-01
• 期刊: INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING
• 影响因子: 2.1
• 作者: Wang, Yan;Bai, Li
• 通讯作者: Bai, Li