Group-Wise Cortical Surface Parcellation Based on Inter-Subject Fiber Clustering.

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.

Inter-Subject Clustering of Brain Fibers from Whole-Brain Tractography.

This work presents an effective multiple subject clustering method using whole-brain tractography datasets. The method is able to obtain fiber clusters that are representative of the population. The proposed approach first applies a fast intra-subject clustering algorithm on each subject obtaining the cluster centroids for all subjects. Second, it compresses the collection of centroids to a latent space through the encoder of a trained autoencoder. Finally, it uses a modified HDBSCAN with adjusted parameters on the encoded centroids of all subjects to obtain the final inter-subject clusters. The results shows that the proposed method outperforms other clustering strategies, and it is able to retrieve known fascicles in a reasonable execution time, achieving a precision over 87% and F1 score above 86% on a collection of 20 simulated subjects.