# Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level

> **NIH NIH U01** · UT SOUTHWESTERN MEDICAL CENTER · 2022 · $399,771

## Abstract

Project Summary
 Digital scanning of tissue slides, including both hematoxylin and eosin (H&E)-stained and
immunohistochemistry (IHC)-stained slides, is becoming a routine clinical procedure. Technological advances
in imaging, computing and molecular profiling have enabled in-depth tissue characterization at single-cell
resolution while retaining the cell spatial information and its histological context. The confluence of these
developments has created unprecedented opportunities for studying the relationships among tumor morphology,
molecular events, and clinical outcomes. However, there is a lack of computational tools that can fully utilize the
comprehensive information in tissue images at the single-cell level. The overarching goal of this proposal is to
develop iSEE-Cell (image-based Spatial pattern ExplorEr for Cells), a suite of informatics tools to enable image
data analysis, spatial modeling and data integration at single-cell resolution. In order to achieve this goal, we
have built a strong research team with complementary expertise in image analysis, machine learning, spatial
modelling, single cell genomics, cancer pathology and software development. Specifically, we will: 1. Develop
algorithms to classify different types of cells based on nucleus morphology, that will be applicable to all types of
tissue images. 2. Develop a powerful image restoration tool and quality enhancer for restoring blurred regions,
enhancing low resolution/magnification into high resolution, and normalizing staining colors. 3. Develop and
integrate tissue image analysis, spatial modeling and visualization tools into the iSEE-Cell platform. We will
engage users, including informaticians, oncologists, pathologists, surgeons and cancer biologists, in the process
of algorithm and tool development to collect feedback for the proposed informatics tools. All proposed methods
were motivated by real-world biological and clinical applications. If implemented successfully, the proposed study
will facilitate users in studying the tumor microenvironment and in improving cancer risk assessment, diagnosis,
and outcome prediction.

## Key facts

- **NIH application ID:** 10486136
- **Project number:** 5U01CA249245-02
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Guanghua Xiao
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $399,771
- **Award type:** 5
- **Project period:** 2021-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10486136

## Citation

> US National Institutes of Health, RePORTER application 10486136, Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level (5U01CA249245-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10486136. Licensed CC0.

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