Lopez-Lopez Narciso, Vazquez Andrea, Poupon Cyril, Mangin Jean-Francois, Ladra Susana, Guevara Pamela
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1696-1700. doi: 10.1109/EMBC44109.2020.9175779.
We present GeoSP, a parallel method that creates a parcellation of the cortical mesh based on a geodesic distance, in order to consider gyri and sulci topology. The method represents the mesh with a graph and performs a K-means clustering in parallel. It has two modes of use, by default, it performs the geodesic cortical parcellation based on the boundaries of the anatomical parcels provided by the Desikan-Killiany atlas. The other mode performs the complete parcellation of the cortex. Results for both modes and with different values for the total number of sub-parcels show homogeneous sub-parcels. Furthermore, the execution time is 82s for the whole cortex mode and 18s for the Desikan-Killiany atlas subdivision, for a parcellation into 350 sub-parcels. The proposed method will be available to the community to perform the evaluation of data-driven cortical parcellations. As an example, we compared GeoSP parcellation with Desikan-Killiany and Destrieux atlases in 50 subjects, obtaining more homogeneous parcels for GeoSP and minor differences in structural connectivity reproducibility across subjects.
我们提出了GeoSP，这是一种并行方法，它基于测地距离创建皮质网格的分割，以考虑脑回和脑沟的拓扑结构。该方法用一个图来表示网格，并并行执行K均值聚类。它有两种使用模式，默认情况下，它基于Desikan-Killiany图谱提供的解剖学分割边界执行测地皮质分割。另一种模式执行皮质的完整分割。两种模式以及不同子分割总数值的结果都显示出均匀的子分割。此外，对于分割成350个子分割，整个皮质模式的执行时间为82秒，Desikan-Killiany图谱细分的执行时间为18秒。所提出的方法将可供社区用于执行数据驱动的皮质分割评估。例如，我们在50名受试者中将GeoSP分割与Desikan-Killiany和Destrieux图谱进行了比较，结果表明GeoSP得到的分割更均匀，并且受试者之间结构连通性再现性的差异较小。
