Parallel optimization of fiber bundle segmentation for massive tractography datasets

TL;DR

This paper introduces a parallel algorithm for fiber bundle segmentation in tractography that significantly reduces execution time and memory usage, enabling analysis of larger datasets efficiently.

Contribution

The paper presents a new parallel algorithm that improves execution time and memory efficiency for fiber bundle segmentation in large tractography datasets.

Findings

Segmentation time reduced from 14 to 6 minutes for large datasets.

Memory consumption decreased by approximately 21%.

Achieved a 2.34x acceleration in processing speed.

Abstract

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.