FreeSurfer Development, Maintenance, and Hardening

摘要： 人类大脑的成像在过去20年里经历了爆炸性的增长，主要是通过各种不同的 核磁共振成像的方式。海量的数据需要自动化和强大的分析工具。免费冲浪 (FS，surfer.nmr.mgh.atherard.edu)是用于神经影像分析的卓越工具之一。FS有更多信息 超过44,000次下载，核心FS手稿被引用超过22,000次。文件系统是 NIH资助的许多大型数据采集项目的分析核心，如人类连接组项目 (HCP)、阿尔茨海默病神经成像倡议(ADNI)、弗雷明翰心脏研究(FHS)、青少年 脑认知发展(ABCD)，以及英国生物库。600多种基于ADNI的出版物中有三分之一 引用FS。简而言之，如果没有FS，在神经成像领域进行的许多创新研究都是不可能的。 自1998年开始，FS以提供T1加权MRI的详细和自动化解剖分析而闻名 图像，尤其是皮质表面。然而，FS解剖分析为ALL提供了理想的基础 脑成像模式包括功能磁共振成像、弥散成像、正电子发射计算机断层扫描、光学/近红外光谱成像以及脑电/脑磁图。文件系统 提供执行这些分析的工具以及与其他分析工具(例如，SPM， FSL，Afni)。FS已用于术前计划，甚至在手术室中使用。 最初的拨款主要集中在序列自适应多模式分割(SAMSEG)。 SAMSEG使用参数贝叶斯生成模型对脑图像进行分割。SAMSEG框架 将图谱先验和多变量高斯强度模型与脑图像(包括MRI伪影，如偏差)进行匹配 字段)。SAMSEG可以接受任何通道或通道组合作为输入。由于它采用了强度模型， 它对不同扫描仪的差异具有很强的鲁棒性。由于它是一个生成性模型，因此很容易扩展到包含更多内容 细分详细信息。例如，SAMSEG框架已被用于分割海马区， 杏仁核、丘脑核团和脑外结构。 更新的主要愿景是扩展SAMSEG框架，以适应纵向 模型，包含更多的解剖细节，并使用SAMSEG输出作为皮质表面的基础 与SAMSEG一样，与通道无关并且能够使用通道的任意组合的放置。在……里面 此外，我们提出了一系列新的工具，这些工具将通过以下方式帮助对大型研究进行个人和群体分析 创建特定于研究的模型。除了这项新的技术开发外，我们还请求支持 软件工程、维护和用户支持-平凡而不创新，但影响很大 对于数以千计依赖自由浏览的研究人员来说，支持是至关重要的。

Abstract: Imaging of the human brain has seen explosive growth in the last two decades mainly through the various modalities of MRI. The massive amount of data requires automatic and robust tools for analysis. FreeSurfer (FS, surfer.nmr.mgh.harvard.edu) is one of the preeminent tools used for neuroimage analysis. FS has more than 44,000 downloads, and the core FS manuscripts have been cited more than 22,000 times. FS is part of the analysis core for many NIH-funded large-scale data acquisition projects such as the Human Connectome Project (HCP), Alzheimer's Disease Neuroimaging Initiative (ADNI), Framingham Heart Study (FHS), The Adolescent Brain Cognitive Development (ABCD), as well as the UK BioBank. One third of the 600+ ADNI-based publications cite FS. Simply put, much of the innovative research done in neuroimaging would not be possible without FS. Started in 1998, FS is best known for providing detailed and automated anatomical analysis of T1-weighted MRI images, especially for the cortical surface. However, FS anatomical analysis provides an ideal substrate for all modes of brain imaging including functional MRI, diffusion MRI, PET, optical/NIRS, as well as EEG/MEG. FS provides tools to perform these analyses as well as software to integrate with other analysis tools (e.g., SPM, FSL, AFNI). FS has been used for presurgical planning and even in the operating room. The original grant mostly centered around Sequence Adaptive Multimodal Segmentation (SAMSEG). SAMSEG uses parametric Bayesian generative modeling to segment brain images. The SAMSEG framework fits atlas priors and multivariate Gaussian intensity models to brain images (including MRI artifacts such as bias fields). SAMSEG can take any modality or combination of modalities as input. Since it adapts its intensity model, it is robust to differences in scanner. Since it is a generative model, it is easy to extend to encompass more segmentation details. For example, the SAMSEG framework has been used to segment hippocampal subfield, amygdalar nuclei, thalamic nuclei, and extracerebral structures. The main vision for the renewal is to extend the SAMSEG framework to accommodate longitudinal models, incorporate more anatomical details, and to use SAMSEG output as a basis for cortical surface placement that is, like SAMSEG, modality independent and capable of using any combination of modalities. In addition, we propose a series of new tools that will assist in the individual and group analysis of large studies by creating study-specific models. In addition to this new technical development, we are requesting support for software engineering, maintenance, and user support – mundane and not innovative, but high-impact this type of support is critical to the thousands of researchers who rely on FreeSurfer.