Statistical and Topological Quantification of Shape Features in Biomedical Imaging

生物医学成像中形状特征的统计和拓扑量化

• 批准号: 2283918
• 金额: --
• 依托单位: University of Southampton
• 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: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2283918
• 关键词: Statistical; Topological; Quantification; Shape; Features

This project develops new methods for medical image analysis by combining techniques from statistical and topological data analysis. The overall objective is to robustly detect morphological features in biomedical images obtained by label-free 'finger-printing' vibrational spectroscopy methods and classify the images according to their disease status. Such advanced spectroscopy and novel imaging methods are able to extract the chemical composition of biological systems, including living cells, to an unprecedented level of detail, and thus can be used for accurate medical diagnosis and early detection of diseases. The imaging techniques give multicomponent spectral information bearing chemical and structural information that can be complex to disentangle in a robust non-subjective manner. However, at the moment, the diagnosis depends solely on the clinician's expertise, while machine learning data-driven classifiers are limited by the typically small number of training samples. This project will develop novel statistical models which use topological summaries to create powerful methods for image classification without the need of vast training sets.

该项目通过结合统计和拓扑数据分析的技术来开发用于医学图像分析的新方法。总体目的是在通过无标签的“手指打印”振动光谱法获得的生物医学图像中的形态学特征进行稳健检测形态特征，并根据图像根据其疾病状态对图像进行分类。这种晚期光谱和新型成像方法能够将生物系统（包括活细胞）的化学组成提取到前所未有的细节水平，因此可以用于准确的医学诊断和早期检测疾病。成像技术提供了具有化学和结构信息的多组分光谱信息，这些信息可能会以牢固的非主体方式分解为复杂。但是，目前，诊断仅取决于临床医生的专业知识，而机器学习数据驱动的分类器受通常少数培训样本的限制。该项目将开发新颖的统计模型，这些模型使用拓扑摘要来创建强大的图像分类方法，而无需大量的训练集。