Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 15; no. 1; pp. 71 - 86
• Main Authors: Labra, Nicole, Guevara, Pamela, Duclap, Delphine, Houenou, Josselin, Poupon, Cyril, Mangin, Jean-François, Figueroa, Miguel
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2017; Springer Nature B.V
• Subjects: Adult; Algorithms; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Brain - cytology; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Connectome - methods; Diffusion Magnetic Resonance Imaging - methods; Female; Humans; Image Processing, Computer-Assisted - methods; Male; Neural Pathways - cytology; Neurology; Neurosciences; Original Article; Pattern Recognition, Automated - methods; Software; White Matter - cytology; Young Adult; Tractography segmentation; White matter tracts; GPU programming; Streamline distance; Diffusion-weighted MRI; HARDI data
• ISSN: 1539-2791; 1559-0089
• DOI: 10.1007/s12021-016-9316-7

This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced. The smaller set of remaining streamlines is then segmented using the original metric, thus eliminating any false positives from the preprocessing stage. As a result, a single-thread implementation of the algorithm can segment a dataset of almost 9 million streamlines in less than 6 minutes. Moreover, parallel versions of our algorithm for multicore processors and graphics processing units further reduce the segmentation time to less than 22 seconds and to 5 seconds, respectively. This performance enables the use of the algorithm in truly interactive applications for visualization, analysis, and segmentation of large white matter tractography datasets.