Yang Faguo, Kruggel Frithjof
Signal and Image Processing Laboratory, Department of Biomedical Engineering, University of California, Irvine, CA 92697-2755, United States.
Med Image Anal. 2008 Aug;12(4):442-451. doi: 10.1016/j.media.2008.01.003. Epub 2008 Feb 6.
The neocortical surface has a rich and complex structure comprised of folds (gyri) and fissures (sulci). Sulci are important macroscopic landmarks for orientation on the cortex. A precise segmentation and labeling of sulci is helpful in human brain mapping studies relating brain anatomy and function. Due to their structural complexity and inter-subject variability, this is considered as a non-trivial task. An automatic algorithm is proposed to accurately segment neocortical sulci: vertices of a white/gray matter interface mesh are classified under a Bayesian framework as belonging to gyral and sulcal compartments using information about their geodesic depth and local curvature. Then, vertices are collected into sulcal regions by a watershed-like growing method. Experimental results demonstrate that the method is accurate and robust.
新皮质表面具有由褶皱（脑回）和裂隙（脑沟）组成的丰富而复杂的结构。脑沟是皮质定位的重要宏观标志。脑沟的精确分割和标记有助于进行将脑解剖结构与功能相关联的人脑图谱研究。由于其结构复杂性和个体间差异，这被认为是一项具有挑战性的任务。本文提出了一种自动算法来精确分割新皮质脑沟：在贝叶斯框架下，利用白质/灰质界面网格顶点的测地线深度和局部曲率信息，将其分类为属于脑回和脑沟区域。然后，通过类似分水岭的生长方法将顶点收集到脑沟区域中。实验结果表明该方法准确且稳健。
