BundleSeg: A versatile, reliable and reproducible approach to white
  matter bundle segmentation
Etienne St-Onge Kurt G Schilling Francois Rheault
This work presents BundleSeg, a reliable, reproducible, and fast method for
extracting white matter pathways. The proposed method combines an iterative
registration procedure with a recently developed precise streamline search
algorithm that enables efficient segmentation of streamlines without the need
for tractogram clustering or simplifying assumptions. We show that BundleSeg
achieves improved repeatability and reproducibility than state-of-the-art
segmentation methods, with significant speed improvements. The enhanced
precision and reduced variability in extracting white matter connections offer
a valuable tool for neuroinformatic studies, increasing the sensitivity and
specificity of tractography-based studies of white matter pathways.