Image Analysis Core

图像分析核心

• 批准号: 10045029
• 负责人: Ronny Korstanje
• 金额: $ 9.39万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10045029
• 关键词: Age; Aging; Animals; Biology of Aging; Cell Proliferation; Chronic; Clinical; Clinical Trials; Communities; Computer Assisted; Cost Effectiveness Analysis; Custom; Data; Disease; Female; Funding; Generations; Geroscience; Goals; Heart; Histologic; Histopathology; Image Analysis; Intervention; Kidney; Knock-out; Lesion; Liver; Lung; Machine Learning; Manuals; Measures; Modeling; Molecular; Mus; Organ; Pathologist; Pathology; Physiological; Process; Research; Research Personnel; Resources; Sampling; Scanning; Services; Severities; Shock; Slide; Stains; Structure of parenchyma of lung; System; Testing; Tissues; Training; Validation; age related; aged; analysis pipeline; anti aging; base; body system; clinical phenotype; clinically relevant; early onset; end of life; experimental study; frailty; healthy aging; interest; intervention effect; male; mesangial cell; neoplastic; neural network; novel; open source; pre-clinical research; quantitative imaging; research study; tool; user-friendly

IMAGE ANALYSIS CORE PROJECT SUMMARY Evaluating changes that precede frailty and end of life using histological characterization of age-related lesions augments molecular, cellular, and physiologic data, and provides an understanding of early-onset mechanisms that underlie age-related changes that may eventually have clinical relevance. The overall goal of the Image Analysis Core is to develop and provide resources for the geroscience community to aid in computer-assisted histopathological analysis and discovery of age-related histological features. Recently, the NIA-funded Geropathology Research Network (GRN), established to enhance the translational value of geropathology for preclinical research studies in anti-aging clinical trials, developed and validated a grading system, designated the geropathology grading platform (GGP), for quantification and comparison of histological lesion scores in tissues from aging mice. While implementation of this grading platform by a trained pathologist may be feasible for experiments with small numbers of animals, an automated approach is necessary for experiments consisting of large sample numbers. An automated approach that can provide unbiased analysis of large sample numbers will lead to a more timesaving and cost-effective analysis and generation of more robust data. A quantitative image analysis pipeline that uses machine learning to accurately identify specific features in scanned slides of stained kidneys was recently developed. This quantitative tool can be easily adjusted to allow quantification using the GGP. The Specific Aims of the Image Analysis Core are: Aim 1. Adapt a quantitative pipeline for the analysis of aged heart, liver, and lung tissues by training and establishing classifiers. Currently, scanned slides of mouse kidneys are uploaded and processed into a large number of tiles in TIF format, and then histological features specific for the kidney are identified and automatically fed into ImageJ for quantification. This pipeline will be adapted for aging research by introducing a training set to identify tissue-specific histological features and develop filters for scoring the lesions according to the GGP. Aim 2. Validate the quantitative pipeline using an annotated set of aged mouse tissues from the Geropathology Research Network. Once pipelines specific for heart, liver, and lung are developed and trained, their accuracy and robustness will be validated by analyzing a set of annotated slides provided by the GRN. Aim 3. Develop and distribute to the geroscience community open-source, user-friendly packages for both the quantitative and discovery pipelines with online training. In addition to providing image analysis as a Core service, the pipelines will be made available to the geroscience community so that other investigators can do their own analysis and customize the pipelines for their own research. These quantitative and discovery tools can be trained for use on any tissue or organ and, once adapted, will be invaluable to the geroscience community. Computer-assisted geropathology will be a powerful tool to measure study endpoints as well as determining the effects of intervention in aging studies.

图像分析核心 项目摘要 使用年龄相关病变的组织学特征评估虚弱和生命结束前的变化 增加了分子、细胞和生理学数据，并提供了对早发性机制的理解 这些变化最终可能具有临床意义。图像的总体目标 分析核心是为老年科学界开发和提供资源，以帮助计算机辅助 组织病理学分析和发现与年龄相关的组织学特征。最近，NIA资助的 老年病理学研究网络（GRN），旨在提高老年病理学的转化价值， 抗衰老临床试验中的临床前研究， 开发并验证了一个分级系统，指定 老年病理学分级平台（GGP），用于量化和比较 衰老老鼠的组织。虽然由受过训练的病理学家实施该分级平台可能是可行的， 对于少量动物的实验，自动化方法对于以下实验是必要的： 大量的样本。一种自动化的方法，可以提供大样本数的无偏分析 将带来更节省时间和更具成本效益的分析并生成更强大的数据。定量 图像分析流水线，使用机器学习准确识别扫描的幻灯片中的特定特征， 最近开发了染色的肾脏。这种定量工具可以很容易地调整，以允许量化 使用GGP。图像分析核心的具体目标是：目标1。调整定量管道， 通过训练和建立分类器对老年人的心脏、肝脏和肺组织进行分析。目前， 将小鼠肾脏的扫描载玻片上传并处理成大量TIF格式的切片， 然后识别肾脏特异性的组织学特征并自动输入ImageJ进行定量。 该管道将通过引入训练集来识别组织特异性组织学特征， 根据GGP特征并开发用于对病变进行评分的过滤器。目标二。定量化 使用来自Geropathology Research Network的一组带注释的老年小鼠组织，一旦 开发和训练了心脏、肝脏和肺专用管道，其准确性和鲁棒性将 通过分析GRN提供的一组注释幻灯片进行验证。目标3.开发并分发给 老年科学社区开源，用户友好的软件包，用于定量和发现 管道与在线培训。除了提供图像分析作为核心服务外，管道还将 向老年科学界提供，以便其他调查人员可以进行自己的分析， 为他们自己的研究定制管道。这些定量和发现工具可以经过培训， 任何组织或器官，一旦适应，将是非常宝贵的老年科学界。计算机辅助 老年病理学将是衡量研究终点以及确定 干预衰老研究。