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.
@article{arxiv.1912.11494,
title = {Parallel optimization of fiber bundle segmentation for massive tractography datasets},
author = {Andrea Vázquez and Narciso López-López and Nicole Labra and Miguel Figueroa and Cyril Poupon and Jean-François Mangin and Cecilia Hernández and Pamela Guevara},
journal= {arXiv preprint arXiv:1912.11494},
year = {2019}
}
Comments
This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941, CONICYT PFCHA/ DOCTORADO NACIONAL/2016-21160342, CONICYT FONDECYT 1161427, CONICYT PIA/Anillo de Investigaci\'on en Ciencia y Tecnolog\'ia ACT172121, CONICYT BASAL FB0008 and from CONICYT Basal FB0001