Image Analysis Core

图像分析核心 项目总结 用年龄相关皮损的组织学特征评估衰弱和生命终结之前的变化 增加了分子、细胞和生理数据，并提供了对早发机制的理解 这是与年龄相关的变化的基础，这些变化最终可能具有临床意义。形象的总体目标 分析的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翻译价值 抗衰老临床试验中的临床前研究， 开发并验证了分级系统，指定 老年病理分级平台(GGP)，用于量化和比较组织学损害评分 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，需要一种自动化的方法来进行实验，包括 大样本的数量。一种自动化的方法，可以提供对大样本数量的无偏见分析 这将导致更省时、更具成本效益的分析和生成更稳健的数据。数量上的 使用机器学习准确识别扫描的幻灯片中的特定特征的图像分析管道 染色的肾脏是最近发展起来的。这一量化工具可以很容易地进行调整，以便进行量化 使用GGP。图像分析核心的具体目标是：目标1.调整量化渠道 通过训练和建立分类器对老年心、肝、肺组织进行分析。目前， 扫描的小鼠肾脏幻灯片被上传并处理成大量的TIF格式的瓷砖，以及 然后，识别肾脏特有的组织学特征，并自动将其送入ImageJ进行量化。 这条管道将通过引入训练集来识别特定组织的组织学，从而适用于衰老研究 根据GGP，确定病变的特征并开发用于对病变进行评分的过滤器。目标2.验证量化 使用老年病理学研究网络的一组带注释的老化小鼠组织的管道。一次 专门针对心脏、肝脏和肺的管道被开发和训练，其准确性和健壮性将被 通过分析GRN提供的一组带注释的幻灯片进行了验证。目标3.开发并分发给 老年科学社区开源、用户友好的量化和发现包 提供在线培训的渠道。除了将图像分析作为核心服务提供外，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 为他们自己的研究定制管道。可以对这些量化和发现工具进行培训，以便在 任何组织或器官，一旦适应，对老年科学界将是无价的。计算机辅助 老年病理学将是测量研究终点以及确定 对衰老研究的干预。

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.