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基于标准解剖学术语的人脑皮质脑回和脑沟的自动分割。

Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature.

机构信息

Inserm U930, Tours, France; Université François Rabelais de Tours, Faculté de Médecine, IFR 135 Imagerie fonctionnelle , Tours, France.

出版信息

Neuroimage. 2010 Oct 15;53(1):1-15. doi: 10.1016/j.neuroimage.2010.06.010. Epub 2010 Jun 12.

DOI:10.1016/j.neuroimage.2010.06.010
PMID:20547229
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2937159/
Abstract

Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology.

摘要

精确定位人类大脑皮层的脑沟-脑回结构对于解释形态-功能数据非常重要,但由于大脑的几何复杂性,这需要解剖学专业知识并且非常耗时。由于提供拓扑正确重建的技术,自动识别脑沟-脑回结构的软件已经得到了很大的改进,这些技术允许对人脑进行膨胀视图。在这里,我们使用标准的国际公认的命名法和标准描述了一个完整的皮质表面分区。这个分区可以在 FreeSurfer 包中获得。首先,计算机辅助手工分区将每个顶点分类为脑沟或脑回,然后将它们进一步细分为每个半球的 74 个标签。使用了 12 个数据集来开发规则和算法(本文报道),这些规则和算法生成的标签既符合解剖规则,又符合自动计算分区。最终的分区用于构建一个自动标记整个大脑皮层的图谱。该图谱用于标记另外 12 个数据集,发现其与手动标签具有很好的一致性。本文提出了一种精确定义的方法,用于以标准术语自动标记皮质表面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/235d/2937159/d1bbb3a93d98/nihms213933f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/235d/2937159/49cfaf4dde17/nihms213933f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/235d/2937159/e131e073535a/nihms213933f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/235d/2937159/6d3bae5c71d2/nihms213933f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/235d/2937159/d1bbb3a93d98/nihms213933f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/235d/2937159/49cfaf4dde17/nihms213933f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/235d/2937159/e131e073535a/nihms213933f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/235d/2937159/6d3bae5c71d2/nihms213933f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/235d/2937159/d1bbb3a93d98/nihms213933f4.jpg

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