Improved Interoperability and Dissemination of Software for Simultaneous Truth an

描述(申请人提供)：大脑分割现在是临床和翻译研究的关键工具，并被应用于评估健康、正常发育、衰老和病理中的大脑结构。通过分割对大脑结构进行定量评估已被认为是神经疾病药物和治疗试验中的替代标记。存在许多分割算法，并使用不同的假设和模型，这导致在一系列应用程序和患者组中的不同性能。需要能够快速有效地评估分割的质量和创建分割的分割算法的神经成像信息学软件。与成人成像相比，定量神经成像领域在儿童成像方面的一系列技术相对缺乏经验。因此，我们寻求提供一种神经成像工具，使广泛的用户能够在精确度(重复性)和精确度(基本事实)方面将他们的分割结果和分割方法与其他工具进行比较。这项提议将继续扩展和发展我们现有的基于史泰博算法的图像分割评估工具。在(Warfield，Zou，and Wells 2004)中描述的同步真相和性能水平估计(Staple)算法已被广泛引用，最近被Thomson Essential Science Indicator确定为该领域被引用最多的1%的前1%的快速突破论文。该算法是一种统计估计过程，最初是为了验证图像分割而开发的，也在分割方案的最佳组合和用于配准分割的标签融合方法中得到应用。该算法目前只允许“硬”分割，而不支持带有概率标签的“软”分割。因此，本项目的具体目标是：1.扩展算法，以验证具有概率标签的图像分割；2.继续维护和传播我们的文档、教程和史泰博程序的开源实施。这些具体目标将使我们能够通过NITRC分发软件，维护和增强软件、其文档和教程，并将算法和软件扩展到全新的分段类别。该项目将显著提高该算法在神经成像社区中的易用性，使新用户能够学习对图像分割验证至关重要的概念，以及如何适当使用该软件的实用程序，并扩大用户社区。将该算法扩展到概率标签将允许评估和比较全新类别的分割算法(那些计算概率或“软”分割的算法)。与公共健康相关：这项建议将通过开发和传播同步真相和绩效水平估计算法的功能和可用性方面的关键进展，使公共健康受益。这将使在神经信息学研究中获得的图像的分割得到验证，这将提高研究界使用、解释和应用定量神经图像评估的能力。

DESCRIPTION (provided by applicant): Brain segmentation is now a key tool in clinical and translational research, and is applied to the evaluation of brain structure in health, in normal development, aging and in pathologies. Quantitative evaluation of brain structure by segmentation has been accepted as a surrogate marker in drug and treatment trials of neurological disorders. Many segmentation algorithms exist, and utilize different assumptions and models, which lead to differing performance in a range of applications and patient groups. There is a need for neuroimaging informatics software that enables rapid and effective evaluation of the quality of segmentations and segmentation algorithms that create them. There is a relative lack of experience in the quantitative neuroimaging community with a range of techniques in pediatric imaging as compared to adult imaging. Consequently, we seek to make available a neuroimaging tool that enables a broad range of users to compare their segmentation results and segmentation methods in terms of both precision (reproducibility) and accuracy (ground truth) against others. This proposal will continue to extend and develop our existing tool for the evaluation of image segmentations based on the STAPLE algorithm. The algorithm for Simultaneous Truth and Performance Level Estimation (STAPLE) described in (Warfield, Zou, and Wells 2004) has become widely cited, recently being identified as a Fast Breaking Paper by Thomson Essential Science Indicators as one of the top 1% most cited papers in the field. The algorithm is a statistical estimation procedure, initially developed for the validation of image segmentation, and also finding application in optimal combination of segmentation schemes and in label fusion methods for segmentation by registration. The algorithm currently allows only ''hard'' segmentations, and does not support ''soft'' segmentations with probabilistic labels. Therefore, the specific aims of this project are to: 1. Extend the algorithm to enable validation of image segmentations which have probabilistic labels, and 2. Continue the maintenance and dissemination of our documentation, tutorials and open source implementation of the STAPLE program. These specific aims will enable us to distribute the software through NITRC, maintain and enhance the software, its documentation and tutorials, and extend the algorithm and software to an entirely new class of segmentations. This project will significantly improve the ease of use of the algorithm in the neuroimaging community, enable new users to learn the concepts that are critical to validation of image segmentation, and the practical procedures for how to use the software appropriately, and to grow the user community. The extension of the algorithm to probabilistic labels will allow entirely new classes of segmentation algorithm (those which compute a probabilistic or ''soft'' segmentation) to be assessed and compared. PUBLIC HEALTH RELEVANCE: This proposal will benefit public health by developing and disseminating key advances in functionality and usability of the Simultaneous Truth and Performance Level Estimation algorithm. This will enable validation of the segmentation of images acquired in neuroinformatics studies, which will improve the capacity of the research community to use, interpret and apply quantitative neuroimage assessments.