Lohmann G, von Cramon D Y
Max-Planck-Institute of Cognitive Neuroscience, Leipzig, Germany.
Med Image Anal. 2000 Sep;4(3):179-88. doi: 10.1016/s1361-8415(00)00024-4.
Human brain mapping aims at establishing correspondences between brain function and brain anatomy. One of the most intriguing problems in this field is the high interpersonal variability of human neuroanatomy which makes studies across many subjects very difficult. The cortical folds ('sulci') often serve as landmarks that help to establish correspondences between subjects. In this paper, we will present a method that automatically detects and attributes neuroanatomical names to the cortical folds using image analysis methods applied to magnetic resonance data of human brains. We claim that the cortical folds can be subdivided into a number of substructures which we call sulcal basins. The concept of sulcal basins allows us to establish a complete parcellation of the cortical surface into separate regions. These regions are neuroanatomically meaningful and can be identified from MR data sets across many subjects. Sulcal basins are segmented using a region growing approach. The automatic labelling is achieved by a model matching technique.
人类脑图谱旨在建立脑功能与脑解剖结构之间的对应关系。该领域最引人关注的问题之一是人类神经解剖结构存在高度的个体差异，这使得对众多受试者进行研究变得非常困难。皮质褶皱（“脑沟”）常作为地标，有助于在不同受试者之间建立对应关系。在本文中，我们将介绍一种方法，该方法利用应用于人类大脑磁共振数据的图像分析方法，自动检测皮质褶皱并为其赋予神经解剖学名称。我们认为，皮质褶皱可细分为若干子结构，我们将其称为脑沟盆地。脑沟盆地的概念使我们能够将皮质表面完全分割成不同区域。这些区域具有神经解剖学意义，并且可以从多个受试者的磁共振数据集中识别出来。脑沟盆地使用区域生长法进行分割。自动标记通过模型匹配技术实现。
