Puonti Oula, Iglesias Juan Eugenio, Van Leemput Koen
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
Martinos Center for Biomedical Imaging, MIGH, Harvard Medical School, USA.
Med Image Comput Comput Assist Interv. 2013;16(Pt 1):727-34. doi: 10.1007/978-3-642-40811-3_91.
In this paper we propose a method for whole brain parcellation using the type of generative parametric models typically used in tissue classification. Compared to the non-parametric, multi-atlas segmentation techniques that have become popular in recent years, our method obtains state-of-the-art segmentation performance in both cortical and subcortical structures, while retaining all the benefits of generative parametric models, including high computational speed, automatic adaptiveness to changes in image contrast when different scanner platforms and pulse sequences are used, and the ability to handle multi-contrast (vector-valued intensities) MR data. We have validated our method by comparing its segmentations to manual delineations both within and across scanner platforms and pulse sequences, and show preliminary results on multi-contrast test-retest scans, demonstrating the feasibility of the approach.
在本文中,我们提出了一种使用组织分类中常用的生成参数模型类型进行全脑分割的方法。与近年来流行的非参数多图谱分割技术相比,我们的方法在皮质和皮质下结构中均获得了领先的分割性能,同时保留了生成参数模型的所有优点,包括高计算速度、在使用不同扫描仪平台和脉冲序列时对图像对比度变化的自动适应性,以及处理多对比度(矢量值强度)磁共振数据的能力。我们通过在扫描仪平台和脉冲序列内部及之间将其分割结果与手动勾勒结果进行比较,验证了我们的方法,并展示了多对比度重测扫描的初步结果,证明了该方法的可行性。