BundleSeg: A versatile, reliable and reproducible approach to white matter bundle segmentation
This provides a more reliable tool for neuroinformatic studies, enhancing sensitivity and specificity in tractography-based analyses of white matter pathways.
The paper tackles the problem of segmenting white matter pathways from brain tractography data by introducing BundleSeg, a method that combines iterative registration with a precise streamline search algorithm. The result shows improved repeatability and reproducibility over state-of-the-art methods, with significant speed improvements.
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.