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

在单细胞水平上分析和建模整个幻灯片图像数据的信息学工具

基本信息

批准号：
10677280
负责人：
Guanghua Xiao
金额：
$ 8.2万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10677280
关键词：
Administrative Supplement Advanced Malignant Neoplasm Algorithmic Software Algorithms Architecture Artificial Intelligence Cell model Cells Classification Code Collaborations Communities Computer Assisted Computing Methodologies Consumption Data Data Analyses Data Analytics Data Set Development Educational Curriculum Enhancers Environment Equilibrium Fractionation Funding Goals Head Hematoxylin Hybrids Image Image Analysis Image Enhancement Individual Informatics Knowledge Libraries Location Machine Learning Manuals Maps Masks Methodology Methods Modeling Morphologic artifacts Neoplasm Circulating Cells Network-based Occupations Optics Parents Pattern Positioning Attribute Process Protocols documentation Research Research Personnel Residual state Resolution Resources Running Security Services Signal Transduction Software Design Speed Stains Structure System Technology TensorFlow Testing Time Tissue imaging Training Tumor Tissue Update Variant Vertebral column Visualization Work adaptation algorithm anticancer research application programming interface base cell type cellular imaging computer infrastructure computerized data processing deep learning deep learning algorithm experience experimental study fluid flow fluorescence microscope generative adversarial network handheld mobile device improved informatics tool innovation learning strategy loss of function microchip microscopic imaging novel operation preservation restoration simulation tool user-friendly web services whole slide imaging

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亚型光学显微镜图像中的肿瘤细胞项目概要/摘要父 IMAT 项目 (R21CA240185) 的目标是开发一个用于分馏和分析的新平台 CTC 亚群的研究并阐明 CTC 的转移潜力。目前，这项工作需要研究人员记录微芯片上捕获的数百个细胞的单独显微镜图像，整合所有图像通过流动流体模拟，并分析捕获单元的三个特征（包括角位置、归一化速度和剪切力）用于识别 CTC 亚型。这个过程非常耗费人力和时间，因为大多数步骤依赖于手动操作。 ITCR 项目 (1U01CA249245) 的目标是开发一个信息学平台 iSEE-Cell（基于图像的空间模式 ExplorEr for Cells），具有一套用于单细胞水平组织图像分析、可视化、探索和空间建模的信息学工具。本拟议的行政补充申请旨在支持 IMAT 和 ITCR 之间的合作资助的项目旨在开发基于深度学习的方法，从光学中识别 CTC 的亚型显微镜图像。该提案的基本原理是深度学习方法的发展将提供 HU 结构微芯片上捕获的 CTC 的自动表征和分类。这拟议的合作项目将利用两个项目开发的技术，这将带来共同并增强互补技术平台和方法的能力，以推进癌症研究。所提出方法的创新包括以下内容：1）多种亚型的识别使用 HU 微芯片上的位置信息对 CTC 进行分析，无需进行破坏性免疫染色分析； 2）新颖的 Restore-GAN 模型可提高 CTC 捕获实验中获得的显微镜图像的质量提高 CTC 亚型的预测准确性； 3）拟议的信息学工具将提供计算机辅助自动化工具通过人工智能赋能 CTC 研究。具体目标包括：目标 1：使用显微镜图像和分析/预测结果（来自 IMAT 项目）作为数据输入来测试是否从肿瘤组织图像中对不同类型细胞进行分类的算法（iSEE-Cell，由 ICTR 项目开发）可应用于显微镜图像；目标 2：应用新颖的计算方法（Restore-GAN，开发于 ICTR项目）来提高从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来整合用于远程分析光学/荧光显微镜图像的 iSEE-Cell 平台。自动的能力从显微镜图像中捕获的细胞中提取/分析信息是迫切需要的，并且将显着提高IMAT项目的吞吐量和工作效率。