Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
Neuroimage. 2011 Jul 1;57(1):76-88. doi: 10.1016/j.neuroimage.2011.03.064. Epub 2011 Apr 5.
Automated accurate and consistent sulcal parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains, since longitudinal cortical changes are normally very subtle, especially in aging brains. However, applying the existing methods (which were typically developed for cortical sulcal parcellation of a single cortical surface) independently to longitudinal cortical surfaces might generate longitudinally-inconsistent results. To overcome this limitation, this paper presents a novel energy function based method for accurate and consistent sulcal parcellation of longitudinal cortical surfaces. Specifically, both spatial and temporal smoothness are imposed in the energy function to obtain consistent longitudinal sulcal parcellation results. The energy function is efficiently minimized by a graph cut method. The proposed method has been successfully applied to sulcal parcellation of both real and simulated longitudinal inner cortical surfaces of human brain MR images. Both qualitative and quantitative evaluation results demonstrate the validity of the proposed method.
自动、准确且一致的纵向皮质表面脑沟划分对于研究人类大脑的纵向形态和功能变化非常重要，因为纵向皮质变化通常非常细微，尤其是在衰老的大脑中。然而，将现有的方法（通常是为单个皮质表面的皮质脑沟划分而开发的）独立应用于纵向皮质表面可能会产生不一致的纵向结果。为了克服这一限制，本文提出了一种基于能量函数的新方法，用于准确且一致地对纵向皮质表面进行脑沟划分。具体来说，在能量函数中施加空间和时间平滑性，以获得一致的纵向脑沟划分结果。该能量函数通过图割方法进行高效地最小化。所提出的方法已成功应用于真实和模拟的人脑磁共振图像的纵向内皮质表面的脑沟划分。定性和定量评估结果均证明了该方法的有效性。
