Thalamus parcellation using multi-modal feature classification and thalamic nuclei priors.
Thalamus parcellation using multi-modal feature classification and thalamic nuclei priors.
复制标题
使用多模态特征分类和丘脑核先验进行丘脑分割。
DOI：
10.1117/12.2216987
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Prince,JerryL
中科院分区：
文献类型：
--
作者：
Glaister,Jeffrey;Carass,Aaron;Stough,JoshuaV;Calabresi,PeterA;Prince,JerryL
丘脑和丘脑核的分割对于量化神经退行性疾病的体积变化是有用的。大多数丘脑分割算法仅使用T1加权磁共振图像，当前丘脑分割方法需要手动交互。较小的核，如外侧膝状体和内侧膝状体，由于其尺寸小，定位具有挑战性。我们提出了一种自动分割算法，使用一组来自扩散张量图像（DTI）和丘脑核定位先验的功能。在提取特征之后，训练分层随机森林分类器来定位丘脑。第二个随机森林将丘脑体素分类为属于六个丘脑核类之一。该算法进行了测试，使用留一交叉验证计划，并与国家的最先进的算法进行比较。该算法具有较高的骰子分数相比，其他方法为整个丘脑和几个核。
Segmentation of the thalamus and thalamic nuclei is useful to quantify volumetric changes from neurodegenerative diseases. Most thalamus segmentation algorithms only use T1-weighted magnetic resonance images and current thalamic parcellation methods require manual interaction. Smaller nuclei, such as the lateral and medial geniculates, are challenging to locate due to their small size. We propose an automated segmentation algorithm using a set of features derived from diffusion tensor image (DTI) and thalamic nuclei location priors. After extracting features, a hierarchical random forest classifier is trained to locate the thalamus. A second random forest classifies thalamus voxels as belonging to one of six thalamic nuclei classes. The proposed algorithm was tested using a leave-one-out cross validation scheme and compared with state-of-the-art algorithms. The proposed algorithm has a higher Dice score compared to other methods for the whole thalamus and several nuclei.