Topology-based tumor analysis for medical images

项目摘要/摘要 肿瘤的形态和形态已成为肿瘤诊断和治疗的重要标志物。近期 医学成像技术的发展使人们能够更详细地描述 决议。然而，现有的研究在有限的范围内描述了肿瘤的形状。有一种科学的 需要利用医学图像提高对肿瘤转移的认识，为医学提供新的见解 决策。该项目旨在开发适用于不同类型的医学图像的拓扑性肿瘤形状表示。 CAL图像，并提供了基于拓扑图像特征的基于模型的方法。我们的初步结果 提示医学图像的拓扑特征可以捕捉肿瘤区域的形状和模式，并预测预后。 控制关键临床参数后的预后和存活率。拟议的项目将进一步开发基于拓扑的 为病理和放射图像提供肿瘤分析方法，并提供辅助医疗决策的工具。 该项目的目标将通过三个目标来实现：(1)制定方法，将空间和 肿瘤的形态信息以及基因组特征和拓扑特征之间的关系 针对放射图像的三维胶质瘤；(2)发展了拓扑肿瘤形状分析方法，该方法 分析多个细胞类型区域的形状和相互作用，提取图像大小不变的形状表示。 为二维肺腺癌病理图像提供了一个可访问的资源 通过提供用户友好的软件和教育来研究社区。我们在肺腺癌方面的应用 原发胶质瘤的病理图像和磁共振成像将提供预后信息 超越标准的临床因素，为拓扑肿瘤形态分析的临床应用提供有力证据。 这项提议还将允许南卫理公会大学不同背景的学生体验 广泛的医学研究，包括但不限于涉及肿瘤的基因组和细胞过程， 在使用协作的同时，在医学成像方面进行生存建模、拓扑数据分析和深度学习 来自地区机构的研究人员。

Project Summary/Abstract Tumor shapes and patterns have been used as important markers for cancer diagnosis and treatment. Recent developments in medical imaging technology have enabled a more detailed description of tumor regions in high resolution. However, existing studies have described the shape of tumors in a limited scope. There is a scientific need to enhance understanding of tumor metastasis using medical images and provide a new insight for medical decision-making. This project aims to develop topological tumor shape representations for different types of medi- cal images and provide model-based approaches based on the topological image features. Our preliminary results suggest that topological features of medical images capture shapes and patterns of tumor regions and predict prog- nosis and survival after controlling key clinical parameters. The proposed project will further develop topology-based tumor analysis methods for pathology and radiographic images and provide tools to aid medical decision-making. The objective of the project will be accomplished by three aims: (1) develop methodologies to pair spatial and shape information of tumors and investigate relationships between genomic characteristics and topological features for three-dimensional gliomas of radiographic images; (2) develop topological tumor shape analysis methods that analyze shapes and interactions of multiple cell-type regions and extract image size-invariant shape representa- tions for two-dimensional lung adenocarcinoma pathology images; and (3) provide an accessible resource to the research community by offering user-friendly software and education. Our applications using lung adenocarcinoma pathological images and magnetic resonance imaging of primary gliomas images will provide prognostic information beyond standard clinical factors and serve as strong evidence for clinical usage of topological tumor shape analysis. This proposal will also allow students from various backgrounds at Southern Methodist University to experience a broad spectrum of medical research, including but not limited to genomic and cellular processes involved in tumors, survival modeling, topological data analysis, and deep learning in medical imaging while working with collaborative researchers from regional institutions.