Parallel Optimization of Fiber Bundle Segmentation for Massive Tractography Datasets

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 pr...

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Bibliographic Details
Published inProceedings (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
LanguageEnglish
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
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Summary: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.
ISSN:1945-8452
DOI:10.1109/ISBI.2019.8759208