title: Interactive segmentation of white-matter fibers using a multi-subject atlas.
abstract: We present a fast algorithm for automatic segmentation of white matter fibers from tractography datasets based on a multi-subject bundle atlas. We describe a sequential version of the algorithm that runs on a desktop computer CPU, as well as a highly parallel version that uses a Graphics Processing Unit (GPU) as an accelerator. Our sequential implementation runs 270 times faster than a C++/Python implementation of a previous algorithm based on the same segmentation method, and 21 times faster than a highly optimized C version of the same previous algorithm. Our parallelized implementation exploits the multiple computation units and memory hierarchy of the GPU to further speed up the algorithm by a factor of 30 with respect to our sequential code. As a result, the time to segment a subject dataset of 800,000 fibers is reduced from more than 2.5 hours in the Python/C++ code, to less than one second in the GPU version.
authors.forename: Nicole
authors.initials: N
authors.lastname: Labra
authors.forename: Miguel
authors.initials: M
authors.lastname: Figueroa
authors.forename: Pamela
authors.initials: P
authors.lastname: Guevara
authors.forename: Delphine
authors.initials: D
authors.lastname: Duclap
authors.forename: Josselin
authors.initials: J
authors.lastname: Houenou
authors.forename: Cyril
authors.initials: C
authors.lastname: Poupon
authors.forename: Jean-Francois
authors.initials: JF
authors.lastname: Mangin
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D019992:Databases as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009412:Nerve Fibers / Q000502:physiology*; D013997:Time Factors; D066127:White Matter / Q000033:anatomy & histology*