Developing computational algorithms for histopathological image analysis

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.