Developing computational algorithms for histopathological image analysis

开发组织病理学图像分析的计算算法

• 批准号: 10097119
• 负责人: Guanghua Xiao
• 金额: $ 40.92万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10097119
• 关键词: Algorithmic Software; Algorithms; Architecture; Bayesian Method; Biological; Biology; Biomedical Research; Cell Communication; Cells; Classification; Clinical; Clinical Pathology; Communicable Diseases; Communities; Complex; Computational algorithm; Computer Models; Computing Methodologies; Data; Diagnosis; Disease; Disease Progression; Evaluation; Extracellular Matrix; Genomics; Goals; Graph; Hematoxylin and Eosin Staining Method; Heterogeneity; Histologic; Histopathology; Image; Image Analysis; Imaging technology; Intuition; Liver diseases; Machine Learning; Malignant Neoplasms; Methods; Microscope; Modeling; Molecular; Molecular Profiling; Morphology; Network-based; Pathologic; Pathologist; Pathology; Patient Care; Patients; Pattern; Physics; Procedures; Prognosis; Research; Resolution; Risk Assessment; Scanning; Slide; Spatial Distribution; Stains; Statistical Models; Structure; Texture; Tissue imaging; Tissues; base; cancer type; cell type; clinical application; clinical care; convolutional neural network; data integration; data modeling; deep learning algorithm; digital; digital pathology; disease diagnosis; disorder risk; drug discovery; experience; improved; insight; machine learning method; molecular pathology; multiple datasets; novel; outcome prediction; particle; pathology imaging; predictive modeling; software development; user friendly software; whole slide imaging

Project Summary Histopathology is the cornerstone of disease diagnosis and prognosis. With the advance of imaging technology, whole-slide image (WSI) scanning of tissue slides is becoming a routine clinical procedure and producing a massive amount of data that captures histopathological details in high resolution. Most current pathological image analysis methods, similar to general image analysis approaches, mainly focus on morphology features, such as tissue texture and granularity, but ignore the complex hierarchical structures of tissues. Cells are the fundamental building blocks to tissues. Different types of cells are first organized into cellular components, which together with the extracellular matrix, form different types of tissue architectures. Understanding the interactions among these different types of cells can provide critical insights into biology and disease status. However, there are some major computational challenges: (1) How to identify and classify different types of cells in tissue, (2) how to characterize the highly complex and heterogeneous spatial organization of tissue, and (3) how to integrate histopathology data with other types of data to study disease status and progression. The goal of this proposal is to develop novel computational methods to analyze histopathology image data to study disease status and progression. In order to achieve this goal, we have built a strong research team with complementary expertise in image analysis, machine learning, statistical modeling, and clinical pathology. Specifically, we will develop novel algorithms to: (1) classify different types of cells from histopathology tissue WSI scans, (2) characterize and quantify cell spatial distribution and cell-cell interactions, and (3) integrate histopathology data with other types data to study disease progression. All proposed methods were motivated by real-world biological and clinical applications across different types of diseases, such as liver diseases, infectious diseases, and cancer. If implemented successfully, the proposed study will facilitate the analysis and modeling of data generated from histopathology tissue slides to improve disease risk assessment, diagnosis, and outcome prediction.

项目摘要 组织病理学是疾病诊断和预后的基石。随着成像的发展 技术，整个滑动图像（WSI）扫描组织幻灯片正在成为常规的临床程序，并且 产生大量数据，以高分辨率捕获组织病理学细节。最新 病理图像分析方法，类似于一般图像分析方法，主要关注形态 特征，例如组织纹理和粒度，但忽略了组织的复杂分层结构。细胞 是组织的基本构建块。首先将不同类型的细胞组织成细胞 组件与细胞外基质一起形成不同类型的组织结构。 了解这些不同类型的细胞之间的相互作用可以为生物学和 疾病状况。但是，存在一些主要的计算挑战：（1）如何识别和分类 组织中的不同类型的细胞，（2）如何表征高度复杂和异质的空间 组织的组织，以及（3）如何将组织病理学数据与其他类型的数据整合到研究疾病 地位和进步。该建议的目的是开发新的计算方法来分析 组织病理学图像数据研究疾病状况和进展。为了实现这一目标，我们建立了 一个强大的研究团队，具有图像分析，机器学习，统计建模的互补专业知识， 和临床病理。具体而言，我们将开发出新的算法：（1）从 组织病理组织WSI扫描（2）表征和量化细胞空间分布和细胞 - 细胞相互作用， （3）将组织病理学数据与其他类型的数据整合到研究疾病进展。所有提出的方法 是由不同类型疾病的现实生物学和临床应用的动机，例如肝脏 疾病，传染病和癌症。如果成功实施，拟议的研究将促进 从组织病理学组织幻灯片产生的数据分析和建模，以改善疾病风险评估， 诊断和结果预测。