VALIDATION OF AUTOMATIC SEGMENTATION ALGORITHMS

自动分割算法的验证

• 批准号: 3203204
• 负责人: Charles Raymond Meyer
• 金额: $ 16.62万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-04-01 至 1996-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3203204
• 关键词: artificial intelligence; autoradiography; brain imaging /visualization /scanning; brain neoplasms; computer program /software; histopathology; laboratory rat; magnetic resonance imaging; neuroanatomy

The goals of this proposal are three-fold: 1) acquire realistic, multimodal data sets that can be used for validating quantitative segmentation algorithms, 2) test segmentation algorithms developed under funding from existing grant (IROICA52709), and 3) improve the state of the art in registration through further development of homologous feature definition tools. Realistic validation of segmentation algorithms on data sets with corresponding functional/histological correlation is very important. While validations usually include imaging relatively simplistic phantoms usually constructed from milled shapes in Lucite with doped, water-filled voids, such phantoms grossly under estimate artifacts encountered in routine clinical imaging. We wish to acquire data sets that more closely mimic the clinical setting, but in a setting where ground truth can be known. Imaging rat brains injected with 9L carcinomas, followed by sacrifice and multislice autoradiography which will be reconstructed to mimic FDG PET imaging, will provide multimodal data sets containing both anatomical and functional information. In addition each data set will have at least one correlative histopathologic slide through midlesion where regions of normal tissue, edema, tumor, and necrotic core will be outlined by a pathologist. These data sets will be made available for easy access to the national/international research community. Segmentation algorithms currently under development in our lab through funding from NIH IROICA52709 will be tested using the acquired data sets. These algorithms are more fully described in the preliminary results section of this application. Reported results of testing will include scatter charts of algorithm-computed volumes versus expert-measured volumes, as well as the more sensitive metric of percent non-intersecting volumes between automatic algorithms and expert-defined surfaces. Multivariate segmentation and multimodality registration are intimately connected topics. The accuracy of multivariate segmentation can be greatly enhanced by the addition of registered data sets containing independent information from other modalities, and likewise the registration task can be aided by automatic generation of shape features from an accurate segmentation algorithm. Approaches to registration usually involve either surface correlations, moment matching, or user-defined (usually point) feature matching to estimate the geometric mapping of one data set onto another. Due to the generality of the user-feature identification method, we have developed an affine method to register 3D volume data sets using a weighted least mean square error estimator on features including not only homologous points, but also lines and planes. We wish to continue this work to include use of additional features as well.

该提议的目标是三个方面：1）获得现实， 可用于验证定量的多模式数据集 分割算法，2）在下面开发的测试分割算法 现有赠款（IROICA52709）的资金，3）改善状态 通过进一步开发同源特征的注册艺术 定义工具。 对数据集的分割算法的现实验证 相应的功能/组织学相关性非常重要。 验证通常包括相对简单的幻象成像 通常由卢克特（Lucite）的铣削形状构建 空隙，此类幻影在估计中遇到的文物中遇到的幻象 常规临床成像。 我们希望获取更紧密的数据集 模仿临床环境，但在地面真理的环境中 已知。 成像大鼠大脑注射了9L癌，其次是 牺牲和多层放射自显影，将重建为 模拟FDG PET成像，将提供包含两者的多模式数据集 解剖和功能信息。 另外，每个数据集将 至少有一个相关的组织病理学滑动到中期 正常组织，水肿，肿瘤和坏死核的区域将是 由病理学家概述。 这些数据集将用于 轻松进入国家/国际研究界。 当前在我们实验室开发的细分算法通过 NIH IROICA52709的资金将使用获得的数据集进行测试。 这些算法在初步结果中更全面地描述 此应用程序的部分。 报告的测试结果包括 算法计算量与专家测量的散点图 数量，以及更敏感的非交流百分比度量 自动算法和专家定义的表面之间的量。 多元细分和多模式注册是密切的 连接的主题。 多元分割的准确性可以是 通过添加包含的注册数据集，大大增强了 来自其他方式的独立信息，同样 自动生成形状功能可以帮助注册任务 从准确的分割算法中。 注册方法 通常涉及表面相关，力矩匹配或 用户定义（通常是点）功能匹配以估计几何形状 将一个数据集映射到另一个数据。 由于一般性 用户功能识别方法，我们已经开发了一种仿射方法 使用加权最小平方错误的注册3D卷数据集 估算功能不仅包括同源点，还包括 线和飞机。 我们希望继续这项工作，包括 还有其他功能。