1 research outputs found
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 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.Comment: 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 FB000