Parallel Optimization of Fiber Bundle Segmentation for Massive Tractography Datasets

• Published in: Proceedings (International Symposium on Biomedical Imaging) pp. 178 - 181
• Main Authors: Vazquez, Andrea, Lopez-Lopez, Narciso, Labra, Nicole, Figueroa, Miguel, Poupon, Cyril, Mangin, Jean-Francois, Hernandez, Cecilia, Guevara, Pamela
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2019
• Subjects: bundle atlas; fiber tractography; Image segmentation; Measurement; multi-core; Optimization; Parallel algorithm; Parallel algorithms; Random access memory; White matter; white matter segmentation
• ISSN: 1945-8452
• DOI: 10.1109/ISBI.2019.8759208

We present an optimized algorithm that performs automatic classification of white matter fibers based on a multi-subject bundle atlas. We implemented a parallel algorithm that improves upon its previous version in both execution time and memory usage. Our new version uses the local memory of each processor, which leads to a reduction in execution time. Hence, it allows the analysis of bigger subject and/or atlas datasets. As a result, the segmentation of a subject of 4,145,000 fibers is reduced from about 14 minutes in the previous version to about 6 minutes, yielding an acceleration of 2.34. In addition, the new algorithm reduces the memory consumption of the previous version by a factor of 0.79.