Stefan Winzeck1,2, Ben Glocker1, Virginia F. J. Newcombe2, David K. Menon2, and Marta M. Correia3
1BioMedIA, Department of Computing, Imperial College London, London, United Kingdom, 2Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, United Kingdom, 3MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
1BioMedIA, Department of Computing, Imperial College London, London, United Kingdom, 2Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, United Kingdom, 3MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
Fibre tract segmentation with TractSeg is more precise but less robust than an atlas-based registration approach. FA and MD showed higher variation when data with different acquisition schemes were combined compared to single-scheme data.
Figure 1. Variation in WM Parcellations. JHU: Forceps minor (CC, ROI #9), left inferior longitudinal fascicle (ILF, ROI #12) and superior longitudinal fascicle (SLF, ROI #14); TractSeg: Rostrum (CC, ROI#5), left inferior longitudinal fascicle (ILF, ROI #26) and superior longitudinal fascicle III (SLF, ROI #39). FA and MD were lowest for single-shell acquisition (60x1). Volumes were consistent for JHU, but varied for TractSeg. Note: Atlases do not exactly segment the same volumes.
Table 1. Coefficient of Variation of ROI Means Within and Across Acquisition Schemes. All CV values displayed as mean [min, max] in %.
