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

IMAT-ITCR 合作:开发基于深度学习的方法,从光学显微镜图像中识别循环肿瘤细胞的亚型

基本信息

  • 批准号:
    10675886
  • 负责人:
  • 金额:
    $ 7.19万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-09-01 至 2023-07-31
  • 项目状态:
    已结题

项目摘要

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 will dramatically enhance the throughput and work efficiency of the IMAT project.
IMAT-ITCR合作:开发基于深度学习的方法来识别循环肿瘤亚型

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Deep learning detector for high precision monitoring of cell encapsulation statistics in microfluidic droplets.
  • DOI:
    10.1039/d2lc00462c
  • 发表时间:
    2022-10-25
  • 期刊:
  • 影响因子:
    6.1
  • 作者:
    Gardner, Karl;Uddin, Md Mezbah;Linh Tran;Thanh Pham;Vanapalli, Siva;Li, Wei
  • 通讯作者:
    Li, Wei
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Wei Li其他文献

Light Harvesting and Enhanced Performance of Si Quantum Dot/Si Nanowire Heterojunction Solar Cells
硅量子点/硅纳米线异质结太阳能电池的光收集和性能增强
  • DOI:
    10.1002/ppsc.201500192
  • 发表时间:
    2016-01
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ling Xu;Wei Li;Linwei Yu;Kunji Chen
  • 通讯作者:
    Kunji Chen

Wei Li的其他文献

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{{ truncateString('Wei Li', 18)}}的其他基金

Developing a novel disease-targeted anti-angiogenic therapy for CNV
开发针对 CNV 的新型疾病靶向抗血管生成疗法
  • 批准号:
    10726508
  • 财政年份:
    2023
  • 资助金额:
    $ 7.19万
  • 项目类别:
Integrative genomic and functional genomic studies to connect variant to function for CAD GWAS loci
整合基因组和功能基因组研究,将 CAD GWAS 位点的变异与功能联系起来
  • 批准号:
    10639274
  • 财政年份:
    2023
  • 资助金额:
    $ 7.19万
  • 项目类别:
The Pathophysiological Role of Cerebellar Glia in Rett Syndrome
小脑胶质细胞在 Rett 综合征中的病理生理学作用
  • 批准号:
    10183494
  • 财政年份:
    2021
  • 资助金额:
    $ 7.19万
  • 项目类别:
The role and mechanism of necrosis in glioblastoma
坏死在胶质母细胞瘤中的作用和机制
  • 批准号:
    10097263
  • 财政年份:
    2021
  • 资助金额:
    $ 7.19万
  • 项目类别:
The role and mechanism of necrosis in glioblastoma
坏死在胶质母细胞瘤中的作用和机制
  • 批准号:
    10330992
  • 财政年份:
    2021
  • 资助金额:
    $ 7.19万
  • 项目类别:
The Pathophysiological Role of Cerebellar Glia in Rett Syndrome
小脑胶质细胞在 Rett 综合征中的病理生理学作用
  • 批准号:
    10591567
  • 财政年份:
    2021
  • 资助金额:
    $ 7.19万
  • 项目类别:
The role and mechanism of necrosis in glioblastoma
坏死在胶质母细胞瘤中的作用和机制
  • 批准号:
    10553723
  • 财政年份:
    2021
  • 资助金额:
    $ 7.19万
  • 项目类别:
The Pathophysiological Role of Cerebellar Glia in Rett Syndrome
小脑胶质细胞在 Rett 综合征中的病理生理学作用
  • 批准号:
    10380144
  • 财政年份:
    2021
  • 资助金额:
    $ 7.19万
  • 项目类别:
A new drug entity for combination therapy of diabetic retinopathy
糖尿病视网膜病变联合治疗的新药物实体
  • 批准号:
    10255782
  • 财政年份:
    2021
  • 资助金额:
    $ 7.19万
  • 项目类别:
Regio- and Enantioselective Alkene Difunctionalizations for the Synthesis of Bioactive Molecules.
用于合成生物活性分子的区域选择性和对映选择性烯烃双官能化。
  • 批准号:
    10046958
  • 财政年份:
    2020
  • 资助金额:
    $ 7.19万
  • 项目类别:
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