Image Analysis Core

图像分析核心

• 批准号: 10425459
• 负责人: Ronny Korstanje
• 金额: $ 9.6万
• 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/10425459
• 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 项目概要 使用与年龄相关的病变的组织学特征来评估衰弱和生命终结之前的变化 增强分子、细胞和生理数据，并提供对早发机制的理解 这是与年龄相关的变化的基础，最终可能具有临床意义。形象总体目标 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。近日，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 抗衰老临床试验的临床前研究， 开发并验证了一个分级系统，指定为 老年病理学分级平台（GGP），用于量化和比较组织学病变评分 来自衰老小鼠的组织。虽然由训练有素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： 大样本数量。一种可以对大量样本进行公正分析的自动化方法 将导致更省时、更具成本效益的分析和生成更可靠的数据。定量的 图像分析管道，使用机器学习来准确识别扫描幻灯片中的特定特征 最近开发了染色肾脏。该定量工具可以轻松调整以进行定量 使用 GGP。图像分析核心的具体目标是： 目标 1. 调整定量流程 通过训练和建立分类器来分析老化的心脏、肝脏和肺组织。现在， 上传小鼠肾脏的扫描切片并处理成大量 TIF 格式的图块，以及 然后识别肾脏特有的组织学特征并自动输入 ImageJ 进行量化。 该管道将​​通过引入识别组织特异性组织学的训练集来适应衰老研究 特征并开发过滤器，根据 GGP 对病变进行评分。目标 2. 验证定量 使用来自老年病理学研究网络的一组带注释的老年小鼠组织进行管道。一次 开发并训练了专门针对心脏、肝脏和肺的管道，其准确性和鲁棒性将得到提高 通过分析 GRN 提供的一组带注释的幻灯片进行验证。目标 3. 开发并分发给 老年科学社区开源、用户友好的定量和发现包 具有在线培训的管道。除了提供图像分析作为核心服务外，管道还将 提供给老年科学界，以便其他研究人员可以进行自己的分析和 为自己的研究定制管道。这些定量和发现工具可以经过培训以用于 任何组织或器官，一旦适应，将对老年科学界具有无价的价值。计算机辅助 老年病理学将成为衡量研究终点以及确定研究效果的有力工具 干预衰老研究。