Image Analysis and Machine Learning for OCT Image Sequences.

OCT 图像序列的图像分析和机器学习。

• 批准号: 2259603
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2259603
• 关键词: Image; Analysis; Machine; Learning; OCT

The eye provides a window through which structural and functional characteristics of blood vessels can be visualised and quantified using new technology. This has potential impacts for a wide range of clinical medical problems. This project will develop computational tools for data analysis so that clinically actionable quantitative information can be derived from retinal images. Optical coherence tomography (OCT) is a commonly used technique for capturing three-dimensional volumetric measurements of the retina. Manual segmentation of the edges in OCT images to detect retinal layers is time-consuming work. There has been some success in using deep learning techniques with such images, however such approaches rely on large amounts of training data and have not been used to segment the full set of layers present in the image. In this project we propose to develop image processing and machine learning techniques to build computational models capable of quickly processing an input image, and providing meaningful layering information about the image, including maps of layer thicknesses and volumes across the macula. These techniques substantially benefit from relying on the known underlying structure of the image available via image processing, which does not need a large training set of labelled images to work effectively.

眼睛提供了一个窗口，通过该窗口可以使用新技术对血管的结构和功能特征进行可视化和量化。这对广泛的临床医学问题具有潜在影响。该项目将开发用于数据分析的计算工具，以便可以从视网膜图像中获取临床上可行的定量信息。光学相干断层扫描 (OCT) 是一种捕获视网膜三维体积测量的常用技术。对 OCT 图像中的边缘进行手动分割以检测视网膜层是一项耗时的工作。在对此类图像使用深度学习技术方面已经取得了一些成功，但是此类方法依赖于大量训练数据，并且尚未用于分割图像中存在的完整层集。在这个项目中，我们建议开发图像处理和机器学习技术，以构建能够快速处理输入图像的计算模型，并提供有关图像的有意义的分层信息，包括黄斑的层厚度和体积图。这些技术很大程度上受益于通过图像处理获得的已知图像底层结构，不需要大量的标记图像训练集即可有效工作。