Robust Cerebrum and Cerebellum Segmentation for Neuroimage Analysis

用于神经图像分析的稳健大脑和小脑分割

• 批准号: 7922035
• 负责人: Jerry L Prince
• 金额: $ 24.42万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7922035
• 关键词: Algorithms; Atlases; Automatic Data Processing; Biomedical Computing; Brain; Cephalic; Cerebellum; Cerebrum; Classification; Code; Collaborations; Communities; Complement; Computer software; Data; Environment; Excision; Funding; Human; Image; Image Analysis; Java; Knowledge; Laboratories; Magnetic Resonance Imaging; Medical; Methodology; Methods; NIH Program Announcements; Neurosciences; Performance; Process; Reporting; Research; Research Project Grants; Science; Software Engineering; Software Tools; Source Code; Staging; System; Testing; Thick; Tissues; Universities; Weight; Writing; brain volume; computerized data processing; cranium; design; gray matter; image processing; imaging Segmentation; innovation; morphometry; neuroimaging; novel; open source; public health relevance; software development; symposium; tool

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.

描述（由申请人提供）：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（也称为颅骨剥离）可能对后续分析产生非常负面的影响，对于一些敏感的研究，手动辅助提取仍然是唯一可行的选择。 尽管有许多报告的算法和几个软件包可用于研究目的，这些方法的性能仍然存在广泛的差异，没有一个是足够的全面分析，涉及整个人类大脑和大量的研究。 该R21将为神经科学界开发、编码、测试和分发SPECTRE（额外颅组织移除的简单范例）软件。 根据计划公告PAR-08-183，作为R21资助，与NA-MIC国家生物医学计算中心的探索性合作将在“NA-MIC Kit”软件环境中共同开发SPECTRE软件，并将其作为源代码和平台特定的可执行文件免费提供。 底层的SPECTRE图像处理算法是最近在约翰霍普金斯大学的图像分析和社区实验室（IACL）中开发和验证的，并已在一个领先的会议上报告。 SPECTRE在多个图谱的使用，模糊分类，分水岭分割和形态图像分割的组合使用，以及对意外去除皮质灰质的高惩罚的强调方面具有创新性。 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）将完成隔离和建立小脑坐标系的算法; 3）将针对不同采集的T1加权数据测试和优化新代码; 4）对SPECTRE算法和现有算法进行了广泛的比较。 结果将是一种算法，该算法可以获取任意T1加权MR脑体积并返回包含小脑、大脑或两者的体积，并且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大脑变化的非常敏感的研究而设计的，这使得我们在分离步骤中对错误的灰质损失进行了非常高的惩罚。 出于这个原因，也因为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。 公共卫生相关性：脑科学的许多进展都是通过使用大脑的磁共振图像发现的。 这些数据的自动处理通常涉及任何特定研究中的大量受试者，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该过程中关键的第一步。 该研究项目将提供软件，免费提供给公众，自动识别人脑的大脑和小脑，以便进行进一步的分析，包括传统的和潜在的新颖的。

项目成果

Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities.

医学图像分析框架的接口和集成：挑战和机遇。

• DOI: 10.1109/bsec.2010.5510850
• 发表时间: 2010
• 期刊: Annual ORNL Biomedical Science and Engineering Center Conference. ORNL Biomedical Science and Engineering Center Conference
• 作者: Covington,Kelsie;McCreedy,EvanS;Chen,Min;Carass,Aaron;Aucoin,Nicole;Landman,BennettA
• 通讯作者: Landman,BennettA