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

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 项目摘要/摘要 母公司IMAT项目(R21CA240185)的目标是开发一个用于分馏和剖面分析的新平台 对CTC亚群进行分类，阐明CTC的转移潜能。目前，这项工作需要研究人员 要记录在微芯片上捕获的数百个细胞的单独显微镜图像，请整合所有图像 利用流动流体模拟，分析了捕获单元的三个特征(包括角位置、归一化 速度和剪切力)来识别CTC亚型。这个过程非常耗费人力和时间， 因为大多数步骤都依赖于人工操作。ITCR项目(1U01CA249245)的目标是开发一种 信息学平台ISEE-Cell(基于图像的细胞空间模式浏览器)，它具有一套 用于单细胞水平的组织图像分析、可视化、探索和空间建模的信息学工具。 这项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，以识别CTC亚型和光学亚型 显微镜图像。这一提议背后的理由是，深度学习方法的发展 将提供在HU结构微芯片上捕获的四氯化碳的自动表征和分类。这 拟议的协作项目将利用两个项目开发的技术，这将带来 共同提升互补技术平台和方法的能力，推动 癌症研究。提出的方法的创新之处包括：1)多亚型识别 使用安虎微芯片上的位置信息对CTC进行检测，而不进行破坏性免疫染色分析；2) 一种改进CTC捕获实验中获得的显微镜图像质量的新型Restore-GaN模型 提高CTC亚型预测的准确性；3)拟议的信息学工具将提供计算机- 辅助自动化工具，为CTC研究提供人工智能支持。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，以测试 从肿瘤组织图像中分类不同类型细胞的算法(ISEE-Cell，卢旺达问题国际法庭项目开发) 可用于显微镜图像；目标2：应用新的计算方法(Restore-GaN，开发于 卢旺达问题国际法庭项目)，以提高从卢旺达问题国际法庭项目获得的图像的图像质量，并测试它们是否 可以提高CTC亚型的预测精度；目标3：开发一个用户友好的界面，将 ISEE-用于远程分析光学/荧光显微镜图像的细胞平台。能够自动地 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，并且将极大地 提高IMAT项目的吞吐量和工作效率。

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.