Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2655-2659. doi: 10.1109/EMBC46164.2021.9631099.
We present an automatic algorithm for the group-wise parcellation of the cortical surface. The method is based on the structural connectivity obtained from representative brain fiber clusters, calculated via an inter-subject clustering scheme. Preliminary regions were defined from cluster-cortical mesh intersection points. The final parcellation was obtained using parcel probability maps to model and integrate the connectivity information of all subjects, and graphs to represent the overlap between parcels. Two inter-subject clustering schemes were tested, generating a total of 171 and 109 parcels, respectively. The resulting parcels were quantitatively compared with three state-of-the-art atlases. The best parcellation returned 69 parcels with a Dice similarity coefficient greater than 0.5. To the best of our knowledge, this is the first diffusion-based cortex parcellation method based on whole-brain inter-subject fiber clustering.
我们提出了一种用于皮质表面的群组分割的自动算法。该方法基于通过跨主体聚类方案计算的代表性脑纤维簇获得的结构连通性。初步区域是从聚类-皮质网格交点定义的。最终的分割是使用包裹概率图来对所有主体的连通性信息进行建模和整合，并使用图来表示包裹之间的重叠。测试了两种跨主体聚类方案，分别产生了总共 171 个和 109 个包裹。将得到的包裹与三个最先进的图谱进行了定量比较。最好的分割返回了 69 个具有大于 0.5 的 Dice 相似性系数的包裹。据我们所知，这是第一个基于全脑跨主体纤维聚类的基于扩散的皮质分割方法。
