Medical Image Processing Laboratory, Biomedical Engineering Department, Tel-Aviv University, 69978 Tel-Aviv, Israel.
IEEE Trans Med Imaging. 2011 Jan;30(1):131-45. doi: 10.1109/TMI.2010.2067222. Epub 2010 Aug 16.
A supervised framework is presented for the automatic registration and segmentation of white matter (WM) tractographies extracted from brain DT-MRI. The framework relies on the direct registration between the fibers, without requiring any intensity-based registration as preprocessing. An affine transform is recovered together with a set of segmented fibers. A recently introduced probabilistic boosting tree classifier is used in a segmentation refinement step to improve the precision of the target tract segmentation. The proposed method compares favorably with a state-of-the-art intensity-based algorithm for affine registration of DTI tractographies. Segmentation results for 12 major WM tracts are demonstrated. Quantitative results are also provided for the segmentation of a particularly difficult case, the optic radiation tract. An average precision of 80% and recall of 55% were obtained for the optimal configuration of the presented method.
提出了一种有监督的框架，用于自动注册和分割从大脑 DT-MRI 中提取的白质 (WM) 束轨迹。该框架依赖于纤维之间的直接注册，而不需要任何基于强度的注册作为预处理。恢复仿射变换以及一组分割纤维。最近引入的概率提升树分类器用于分割细化步骤，以提高目标束分割的精度。所提出的方法与基于强度的先进算法相比，在 DTI 束轨迹的仿射配准方面表现出色。演示了 12 条主要 WM 束的分割结果。还提供了特别困难的情况下，即视辐射束分割的定量结果。对于所提出方法的最佳配置，获得了 80%的平均精度和 55%的召回率。
