Developing computational algorithms for histopathological image analysis

项目摘要 组织学是疾病诊断和预后的基石。随着成像技术的进步 技术，组织载玻片的全载玻片成像（WSI）扫描正在成为常规临床程序， 产生大量的数据，以高分辨率捕获组织病理学细节。最新 病理图像分析方法与一般的图像分析方法类似，主要集中在形态学上 特征，如组织纹理和粒度，但忽略了组织的复杂层次结构。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 细胞外基质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提供对生物学的重要见解， 疾病状态。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以研究疾病 状态和进展。该提案的目标是开发新的计算方法来分析 组织病理学成像数据以研究疾病状态和进展。为了实现这一目标，我们建立了 一个强大的研究团队，在图像分析，机器学习，统计建模， 和临床病理学。具体来说，我们将开发新的算法来：（1）将不同类型的细胞分类， 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-细胞相互作用， 以及（3）将组织病理学数据与其他类型的数据相结合以研究疾病进展。所有提议的方法 受不同类型疾病（如肝脏）的真实生物学和临床应用的启发， 疾病、传染病和癌症。如能成功推行，建议的研究将有助 分析和建模从组织病理学组织载玻片生成的数据以改进疾病风险评估， 诊断和结果预测。

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.