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

> **NIH NIH U01** · UT SOUTHWESTERN MEDICAL CENTER · 2022 · $82,000

## Abstract

IMAT-ITCR Collaboration: Develop deep learning-based methods to identify subtypes of circulating
tumor cells from optical microscope images
Project Summary/Abstract
The goal of the parent IMAT project (R21CA240185) is to develop a new platform for fractionation and profiling
of CTC subpopulations and elucidate the metastatic potential of CTCs. Currently, this work requires researchers
to record hundreds of individual microscope images of the cells captured on the microchip, integrate all images
with flow fluid simulations, and analyze three features of the capture cells (including angular position, normalized
velocity and shear) for identification of CTC subtypes. This process is very labor-intensive and time-consuming,
as most of the steps rely on manual operations. The goal of the ITCR project (1U01CA249245) is to develop an
informatics platform, iSEE-Cell (image-based Spatial pattern ExplorEr for Cells), which features a suite of
informatics tools for tissue image analysis, visualization, exploration and spatial modeling at the single-cell level.
This proposed Administrative Supplement application in support of collaboration between IMAT and ITCR-
funded projects aims to develop deep learning-based methods to identify subtypes of CTCs from optical
microscope images. The rationale underlying this proposal is that the development of deep learning methods
will provide automatic characterization and classification of CTC captured on HU structured microchips. This
proposed collaborative project will leverage the technologies developed by both projects, which will bring
together and enhance the capabilities of complementary technology platforms and methodologies to advance
cancer research. Innovation of the proposed methods include the following: 1) Identification of multiple subtypes
of CTCs using their location information on an HU microchip without destructive immunostaining analysis; 2)
Novel Restore-GAN model to improve quality of microscope image obtained in CTC capture experiments and
enhance predication accuracy for CTC subtypes; 3) The proposed informatics tools will provide computer-
assisted automated tools to empower CTC research with artificial intelligence. Specific aims include: Aim 1:
Using the microscope images and analysis/prediction results (from the IMAT project) as data input to test whether
algorithms to classify different types of cell from tumor tissue images (iSEE-Cell, developed in the ICTR project)
can be applied for microscope images; Aim 2: Apply novel computational methods (Restore-GAN, developed in
the ICTR project) to improve image quality of the images obtained from the IMAT project, and test whether they
can improve prediction accuracy for CTC subtypes; Aim 3: Develop a user-friendly interface to incorporate the
iSEE-Cell platform for analyzing optical/fluorescent microscope images remotely. The ability to automatically
extract/analyze information from captured cells in the microscope images is urgently needed and w...

## Key facts

- **NIH application ID:** 10677280
- **Project number:** 3U01CA249245-02S2
- **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:** $82,000
- **Award type:** 3
- **Project period:** 2021-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10677280

## Citation

> US National Institutes of Health, RePORTER application 10677280, Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level (3U01CA249245-02S2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10677280. Licensed CC0.

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