title: 3D-SSF: A bio-inspired approach for dynamic multi-subject clustering of white matter tracts.
abstract: There is growing interest in the study of white matter (WM) variation across subjects, and in particular the analysis of specific WM bundles, to better understand brain development and aging, as well as to improve early detection of some diseases. Several WM multi-subject clustering methods have been proposed to study WM bundles. These methods aim to overcome the complexity of the problem, which includes the huge size of the WM tractography datasets generated from multiple subjects, the existence of various streamlines with different positions, lengths and geometric forms, as well as the presence of outliers. However, the current methods are not sufficiently flexible to address all of these constraints. Here we introduce a novel dynamic multi-subject clustering framework based on a distributed multiagent implementation of the Multiple Species Flocking model, that we name 3D-Streamlines Stream Flocking (3D-SSF). Specifically, we consider streamlines from different subjects as data streams, and each streamline is assigned to a mobile agent. Agents work together following flocking rules in order to form a flock. Thanks to a similarity function, the agents that are associated with similar streamlines form a flock, whereas the agents that are associated with dissimilar streamlines are considered outliers. We use various experiments performed on noisy synthetic and real human brain data to validate 3D-SSF and demonstrate that it is more efficient and robust to outliers compared to other classical approaches. 3D-SSF is able to extract WM bundles at a population level, while considering WM variation across subjects and eliminating outlier streamlines.
authors.affiliation: USTHB University, FEI, LRPE, ParIMéd, Algiers, Algeria. Electronic address: achekir@usthb.dz.
authors.forename: A
authors.initials: A
authors.lastname: Chekir
authors.affiliation: Université Lyon 1, LIRIS, UMR5205, F-69622, France.
authors.forename: S
authors.initials: S
authors.lastname: Hassas
authors.affiliation: Sherbrooke Connectivity Imaging Lab, Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada.
authors.forename: M
authors.initials: M
authors.lastname: Descoteaux
authors.affiliation: Sherbrooke Connectivity Imaging Lab, Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada.
authors.forename: M
authors.initials: M
authors.lastname: Côté
authors.affiliation: Sherbrooke Connectivity Imaging Lab, Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada.
authors.forename: E
authors.initials: E
authors.lastname: Garyfallidis
authors.affiliation: USTHB University, FEI, LRPE, ParIMéd, Algiers, Algeria.
authors.forename: F
authors.initials: F
authors.lastname: Oulebsir-Boumghar
mesh_terms: D000465:Algorithms; D032701:Biomimetics / Q000379:methods; D001921:Brain / Q000033:anatomy & histology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D066127:White Matter / Q000033:anatomy & histology*
keywords: Data stream; Multi-agent system; Multi-subject clustering; Multiple species flocking model; Outliers; White matter tractography