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Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level

在单细胞水平上分析和建模整个幻灯片图像数据的信息学工具

基本信息

批准号：
10681472
负责人：
Guanghua Xiao
金额：
$ 38.69万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10681472
关键词：
Advanced Malignant Neoplasm Algorithms Biological Cancer Biology Cell Nucleus Cell model Cells Classification Clinical Collaborations Color Communities Complex Computational algorithm Computer Models Computing Methodologies Data Data Analyses Data Set Development Diagnosis Enhancers Ensure Event Feedback Genomics Goals Hematoxylin and Eosin Staining Method Histologic Image Image Analysis Imaging technology Immune Immunofluorescence Immunologic Immunohistochemistry Informatics Infrastructure Machine Learning Malignant Neoplasms Methods Modeling Molecular Molecular Profiling Morphology Non-Malignant Oncologist Outcome Pathologist Pathology Pattern Procedures Process Research Resolution Risk Assessment Running Scanning Security Slide Stains Standardization Stromal Cells Surgeon Technology Tissue imaging Tissues Tumor Tissue Variant Visualization Visualization software algorithm development anticancer research cancer cell cancer risk cancer type cell type classification algorithm clinical application community engagement computer infrastructure computerized tools data integration data management deep learning deep learning algorithm design digital digital pathology experience improved informatics tool insight novel outcome prediction restoration software development tool tool development tumor tumor microenvironment usability user-friendly web services whole slide imaging

项目摘要

Project Summary Digital scanning of tissue slides, including both hematoxylin and eosin (H&E)-stained and immunohistochemistry (IHC)-stained slides, is becoming a routine clinical procedure. Technological advances in imaging, computing and molecular profiling have enabled in-depth tissue characterization at single-cell resolution while retaining the cell spatial information and its histological context. The confluence of these developments has created unprecedented opportunities for studying the relationships among tumor morphology, molecular events, and clinical outcomes. However, there is a lack of computational tools that can fully utilize the comprehensive information in tissue images at the single-cell level. The overarching goal of this proposal is to develop iSEE-Cell (image-based Spatial pattern ExplorEr for Cells), a suite of informatics tools to enable image data analysis, spatial modeling and data integration at single-cell resolution. In order to achieve this goal, we have built a strong research team with complementary expertise in image analysis, machine learning, spatial modelling, single cell genomics, cancer pathology and software development. Specifically, we will: 1. Develop algorithms to classify different types of cells based on nucleus morphology, that will be applicable to all types of tissue images. 2. Develop a powerful image restoration tool and quality enhancer for restoring blurred regions, enhancing low resolution/magniﬁcation into high resolution, and normalizing staining colors. 3. Develop and integrate tissue image analysis, spatial modeling and visualization tools into the iSEE-Cell platform. We will engage users, including informaticians, oncologists, pathologists, surgeons and cancer biologists, in the process of algorithm and tool development to collect feedback for the proposed informatics tools. All proposed methods were motivated by real-world biological and clinical applications. If implemented successfully, the proposed study will facilitate users in studying the tumor microenvironment and in improving cancer risk assessment, diagnosis, and outcome prediction.

项目摘要组织载玻片的数字扫描，包括苏木精和伊红（H&E）染色，免疫组织化学（IHC）染色的载玻片正在成为常规的临床程序。技术进步在成像、计算和分子分析方面，分辨率，同时保留细胞空间信息及其组织学背景。这些因素的汇合这些发展为研究肿瘤形态学之间的关系创造了前所未有的机会，分子事件和临床结果。然而，缺乏可以充分利用这些信息的计算工具。在单细胞水平的组织图像中的全面信息。本提案的总体目标是开发iSEE-Cell（基于图像的细胞空间模式探索器），这是一套信息学工具，单细胞分辨率的数据分析、空间建模和数据集成。为了实现这一目标，我们我已经建立了一个强大的研究团队，在图像分析，机器学习，空间建模、单细胞基因组学、癌症病理学和软件开发。具体来说，我们将：1。发展基于细胞核形态对不同类型的细胞进行分类的算法，这将适用于所有类型的细胞。组织图像。2.开发一个强大的图像恢复工具和质量增强器，用于恢复模糊区域，将低分辨率/放大率增强为高分辨率，并使染色颜色标准化。3.开发和将组织图像分析、空间建模和可视化工具集成到iSEE-Cell平台中。我们将让包括信息学家、肿瘤学家、病理学家、外科医生和癌症生物学家在内的用户参与这一过程算法和工具的开发，以收集反馈的建议信息学工具。所有提议的方法是由真实世界的生物学和临床应用所激发的。如果成功实施，将有助于用户研究肿瘤微环境和改善癌症风险评估，诊断，和结果预测。