Parallel optimization of fiber bundle segmentation for massive tractography datasets
This incremental improvement enables faster and more memory-efficient analysis of large brain imaging datasets for neuroscience researchers.
The researchers tackled the problem of efficiently segmenting massive white matter fiber tractography datasets by developing a parallel optimization algorithm that reduces execution time from about 14 minutes to 6 minutes (2.34x acceleration) and cuts memory consumption by a factor of 0.79 for a dataset of 4,145,000 fibers.
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.