# IMAT-ITCR Collaboration: Develop deep learning-based methods to identify subtypes of circulating tumor cells from optical microscope images

> **NIH NIH R21** · TEXAS TECH UNIVERSITY · 2022 · $71,919

## 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 wi...

## Key facts

- **NIH application ID:** 10675886
- **Project number:** 3R21CA240185-01A1S1
- **Recipient organization:** TEXAS TECH UNIVERSITY
- **Principal Investigator:** Wei Li
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $71,919
- **Award type:** 3
- **Project period:** 2022-09-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10675886

## Citation

> US National Institutes of Health, RePORTER application 10675886, IMAT-ITCR Collaboration: Develop deep learning-based methods to identify subtypes of circulating tumor cells from optical microscope images (3R21CA240185-01A1S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10675886. Licensed CC0.

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