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

• 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
• 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

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流通的亚型 光学显微镜图像的肿瘤细胞 项目摘要/摘要 父级IMAT项目（R21CA240185）的目标是开发一个新的平台来分馏和分析 CTC亚群并阐明了CTC的转移潜力。目前，这项工作需要研究人员 要记录在微芯片上捕获的细胞的数百个单独的显微镜图像，请整合所有图像 使用流体流体模拟，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）用于识别CTC亚型。这个过程是非常密集的且耗时的， 由于大多数步骤都依赖手动操作。 ITCR项目（1U01CA249245）的目标是开发一个 Informatics平台，ISEE-Cell（基于图像的空间模式探索器），该平台具有一套套件 在单细胞水平上进行组织图像分析，可视化，勘探和空间建模的信息学工具。 该提议的行政补充应用程序支持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，以从光学中识别CTC的亚型 显微镜图像。该提案的基本原理是发展深度学习方法的发展 将提供在HU结构化微芯片上捕获的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，这将带来 一起并增强互补技术平台和方法的能力 癌症研究。提出方法的创新包括以下内容：1）识别多个亚型 CTC使用其在HU微芯片上的位置信息而没有破坏性免疫染色分析； 2） 新型的还原模型，以提高CTC捕获实验中获得的显微镜图像质量和 提高CTC亚型的鉴定准确性； 3）拟议的信息学工具将提供计算机 - 协助自动化工具，以通过人工智能增强CTC研究。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）作为数据输入来测试是否 从肿瘤组织图像中对不同类型的细胞进行分类的算法（ISEE-Cell，在ICTR项目中开发） 可用于显微镜图像； AIM 2：应用新颖的计算方法（Restore-GAN，开发 ICTR项目）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单元格中提取/分析信息，并且会大大需要 提高IMAT项目的吞吐量和工作效率。