# Developing computational algorithms for histopathological image analysis

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2022 · $410,000

## Abstract

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.

## Key facts

- **NIH application ID:** 10314050
- **Project number:** 5R01GM140012-02
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Guanghua Xiao
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $410,000
- **Award type:** 5
- **Project period:** 2021-01-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10314050

## Citation

> US National Institutes of Health, RePORTER application 10314050, Developing computational algorithms for histopathological image analysis (5R01GM140012-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10314050. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*