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：
Wei Li
金额：
$ 7.19万
依托单位：
TEXAS TECH UNIVERSITY
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协作：开发基于深度学习的方法来识别循环肿瘤的亚型光学显微镜图像的单元格项目摘要/摘要父级IMAT项目（R21CA240185）的目标是开发一个新的平台，以分馏和分析CTC 亚群并阐明了CTC的转移潜力。目前，这项工作要求研究人员记录数百个在微芯片上捕获的细胞的单个显微镜图像，将所有图像与流体模拟整合在一起，并分析捕获单元的三个特征（包括角位置，标准化速度和剪切）以识别 CTC亚型的。由于大多数步骤都依赖手动操作，因此此过程非常耗时且耗时。 ITCR项目（1U01CA249245）的目标是开发一个信息平台，即基于图像的空间细胞的模式探索器），该探索器具有一套用于组织图像分析，可视化，探索的信息学工具和单细胞水平的空间建模。该提议的行政补充申请支持 IMAT和ITCR资助的项目之间的协作旨在开发基于学习的深度方法来识别亚型来自光学显微镜图像的CTC。该提议的基本原理是深度学习的发展方法将提供在HU结构微芯片上捕获的CTC的自动表征和分类。这拟议的协作项目将利用这两个项目开发的技术，这些技术将汇集在一起，增强了互补技术平台和方法论的能力，以提高癌症研究。创新提出的方法包括以下内容：1）使用其位置识别CTC的多个亚型有关HU微芯片的信息，而无需破坏性免疫染色分析； 2）新型的还原模型以改进在CTC捕获实验中获得的显微镜图像的质量并提高了CTC亚型的鉴定精度； 3）拟议的信息学工具将提供计算机辅助的自动化工具，以使CTC研究能够使用人工智力。具体目的包括：目标1：使用显微镜图像和分析/预测结果（来自IMAT 项目）作为数据输入，以测试算法是否从肿瘤组织图像分类不同类型的细胞（ISEE-Cell，在ICTR项目中开发的）可以用于显微镜图像；目标2：应用新颖的计算方法（还原 - gan，在ICTR项目中开发），以提高从IMAT项目获得的图像的图像质量，并测试它们是否可以提高CTC亚型的预测准确性；目标3：开发一个用户友好的界面以合并 ISEE细胞平台，用于远程分析光学/荧光显微镜图像。自动的能力迫切需要从显微镜图像中捕获的细胞中提取/分析信息，并将大大增强 IMAT项目的吞吐量和工作效率。