Robust Cerebrum and Cerebellum Segmentation for Neuroimage Analysis

描述(申请人提供)：从结构磁共振图像中提取大脑和小脑是神经图像分析的重要初始步骤。不准确的脑提取(也称为头骨剥离)可能会对后续分析产生非常负面的影响，对于一些敏感的研究，人工辅助提取仍然是唯一可行的选择。尽管有许多已报道的算法和几个可用于研究目的的软件包，但这些方法的性能仍然存在广泛的变异性，没有一种方法足以进行涉及整个人脑的全面分析和大量研究。这款R21将为神经科学界开发、编码、测试和分发SPECTRE(颅外组织切除的简单范例)软件。根据计划公告PAR-08-183，作为R21提供资金，与NA-MIC国家生物医学计算中心的探索性合作，将在“NA-MIC Kit”软件环境中联合开发SPECTRE软件，并将其作为源代码和特定于平台的可执行文件免费提供。约翰·霍普金斯大学图像分析和社区实验室(IACL)最近开发并验证了SPECTRE图像处理算法，并在一次领先的会议上进行了报告。SPECTE的创新之处在于它使用了多个地图集，结合使用了模糊分类、分水岭分割和形态图像分割，并强调了对意外移除皮质灰质的高惩罚。研究和开发工作将实现以下具体目标：1)将使用标准NA-MIC软件方法移植和编写现有代码和所有新代码；2)将完成在小脑上隔离和建立坐标系的算法；3)将针对不同获取的T1加权数据测试和优化新代码；4)将对SPECTRE和现有算法进行广泛比较。其结果将是一种算法，它可以获取任意T1加权的MR脑体积，并返回包含小脑、大脑或两者的体积，并在大脑和小脑上自动建立坐标系。SPECTRE软件工具将是第一个提供大脑、小脑或两者的选择性隔离的软件工具。这也将是第一个在小脑上建立坐标系的自动化方法。应该注意的是，我们开发的优化标准是专门为非常敏感的大脑变化研究设计的，这导致我们在隔离步骤中对错误的灰质丢失进行了非常高的惩罚。出于这个原因，也因为该软件工具将非常强大，易于使用，并将在广泛使用的3D Slicer软件的丰富指定环境中运行，我们预计SPECTRE软件工具将在神经成像社区中非常受欢迎。与公共健康相关：脑科学的许多进步都是通过大脑的磁共振图像发现的。对这些数据的自动处理通常涉及任何给定研究中的大量受试者，这是获得科学或医学知识的必要组成部分，而大脑的自动识别通常是这一过程的关键第一步。这一研究项目将向公众提供免费提供的软件，该软件将自动识别人脑的大脑和小脑，以便进行进一步的分析，包括常规的和潜在的新的分析。

DESCRIPTION (provided by applicant): Extraction of the cerebrum and cerebellum from structural magnetic resonance images is an important initial step in neuroimage analysis. Inaccurate brain extraction (also referred to as skull stripping) can have a very negative effect on subsequent analyses, and for some sensitive studies, manually-assisted extraction remains the only viable option. Despite many reported algorithms and several software packages available for research purposes, there is still widespread variability in the performance of these methods, and none are adequate for comprehensive analyses involving the whole human brain and large numbers of studies. This R21 will develop, code, test, and distribute the SPECTRE (Simple Paradigms for Extra Cranial Tissue REmoval) software for the neuroscience community. Funded as an R21 under the Program Announcement PAR-08-183, this Exploratory Collaboration with the NA-MIC National Center for Biomedical Computing, will jointly develop SPECTRE software within the "NA-MIC Kit" software environment and will make it freely available as both source code and platform specific executables. The underlying SPECTRE image processing algorithm was recently developed and validated in the Image Analysis and Community Laboratory (IACL) at Johns Hopkins University and has been reported in a leading conference. SPECTRE is innovative in its use of multiple atlases, its combined use of fuzzy classification, watershed segmentation, and morphological image segmentation, and its emphasis on a high penalty for accidentally removing cortical gray matter. Research and develop efforts will accomplish the following specific aims: 1) The existing code and all new code will be ported and written using the standard NA-MIC software methodology; 2) The algorithms for isolating and establishing a coordinate system on the cerebellum will be completed; 3) The new code will be tested and optimized for differently acquired T1-weighted data; 4) An extensive comparison between SPECTRE and existing algorithms will be carried out. The result will be an algorithm that can take an arbitrary T1-weighted MR brain volume and return a volume containing the cerebellum, the cerebrum, or both, and with coordinate systems automatically established on the cerebrum and cerebellum. The SPECTRE software tool will be the first to provide selective isolation of the cerebrum, the cerebellum, or both. It will also be the first automated method for establishing a coordinate system on the cerebellum. It should be noted that the optimization criteria we have developed is particularly designed for very sensitive studies of brain changes, which has led us to incorporate a very high penalty on erroneous gray matter loss during the isolation step. For this reason and also because the software tool will be very robust, easy to use, and will function within the richly appointed environment of the widely used 3D Slicer software, we expect that the SPECTRE software tool will grow to be very popular within the neuroimaging community. PUBLIC HEALTH RELEVANCE: Many advances in brain science are discovered using magnetic resonance images of the brain. Automatic processing of these data, often involving very large numbers of subjects in any given study, is a necessary component in gaining scientific or medical knowledge, and automatic identification of the brain is typically a key first step in the process. This research project will provide software, freely available to the public that will automatically identify the cerebrum and cerebellum of the human brain so that further analysis, both conventional and potentially novel, can then be carried out.