Image Analysis and Machine Learning for OCT Image Sequences.

眼睛提供了一个窗口，通过它可以使用新技术可视化和量化血管的结构和功能特征。这可能会对一系列临床医学问题产生影响。这个项目将开发用于数据分析的计算工具，以便从视网膜图像中得出临床上可操作的定量信息。光学相干层析成像(OCT)是获取视网膜三维体积测量的常用技术。人工分割OCT图像中的边缘以检测视网膜层是一项耗时的工作。在对这种图像使用深度学习技术方面已经取得了一些成功，然而，这种方法依赖于大量的训练数据，并且还没有被用于分割图像中存在的全集的层。在这个项目中，我们建议开发图像处理和机器学习技术，以建立能够快速处理输入图像的计算模型，并提供关于图像的有意义的分层信息，包括黄斑上的层厚度和体积图。这些技术实质上受益于依赖于通过图像处理可获得的图像的已知底层结构，其不需要标记图像的大训练集来有效工作。

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.