Automatic labelling of the human cortical surface using sulcal basins

被引:97
作者
Lohmann, G [1 ]
von Cramon, DY [1 ]
机构
[1] Max Planck Inst Cognit Neurosci, D-04103 Leipzig, Germany
关键词
medical image analysis; cortical topography; model matching;
D O I
10.1016/S1361-8415(00)00024-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2000 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:179 / 188
页数:10
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