Improved segmentation reproducibility in group tractography using a quantitative tract similarity measure

The field of tractography is rapidly developing, and many automatic or semiautomatic algorithms have now been devised to segment and visualize neural white matter fasciculi in vivo. However, these algorithms typically need to be given a starting location as input, and their output can be strongly dependent on the exact location of this “seed point”. No robust method has yet been devised for placing these seed points so as to segment a comparable tract in a group of subjects. Here, we develop a measure of tract similarity, based on the shapes and lengths of the two tracts being compared, and apply it to the problem of consistent seed point placement and tract segmentation in group data. We demonstrate that using a single seed point transferred from standard space to each native space produces considerable variability in tractography output between scans. However, by seeding in a group of nearby candidate points and choosing the output with the greatest similarity to a reference tract chosen in advance–a method we refer to as neighborhood tractography–this variability can be significantly reduced.