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.

项目概要 组织病理学是疾病诊断和预后的基石。随着影像学的进步 技术，组织载玻片的全载玻片图像 (WSI) 扫描正在成为常规临床程序，并且 产生大量数据，以高分辨率捕捉组织病理学细节。最新 病理图像分析方法与一般图像分析方法类似，主要关注形态学 特征，例如组织纹理和粒度，但忽略了组织复杂的层次结构。细胞 是组织的基本组成部分。不同类型的细胞首先被组织成细胞 这些成分与细胞外基质一起形成不同类型的组织结构。 了解这些不同类型细胞之间的相互作用可以为生物学和 疾病状况。然而，存在一些主要的计算挑战：（1）如何识别和分类 组织中不同类型的细胞，（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来研究疾病 状态和进展。该提案的目标是开发新的计算方法来分析 用于研究疾病状态和进展的组织病理学图像数据。为了实现这一目标，我们建立了 强大的研究团队，在图像分析、机器学习、统计建模等方面具有互补的专业知识， 和临床病理学。具体来说，我们将开发新的算法来：（1）对不同类型的细胞进行分类 组织病理学组织 WSI 扫描，(2) 表征和量化细胞空间分布和细胞间相互作用， (3)将组织病理学数据与其他类型数据整合以研究疾病进展。所有建议的方法 受到不同类型疾病（例如肝脏）的现实生物和临床应用的启发 疾病、传染病和癌症。如果成功实施，拟议的研究将有助于 对组织病理学组织切片生成的数据进行分析和建模，以改进疾病风险评估， 诊断和结果预测。