Combined tract segmentation and orientation mapping for bundle-specific tractography

• Verfasst von: Wasserthal, Jakob [VerfasserIn]
• Verfasst von: Neher, Peter [VerfasserIn]
• Verfasst von: Hirjak, Dusan [VerfasserIn]
• Verfasst von: Maier-Hein, Klaus H. [VerfasserIn]
• Titel: Combined tract segmentation and orientation mapping for bundle-specific tractography
• Verf.angabe: Jakob Wasserthal, Peter F. Neher, Dusan Hirjak, Klaus H. Maier-Hein
• E-Jahr: 2019
• Jahr: 12 September 2019
• Umfang: 15 S.
• Fussnoten: Gesehen am 30.01.2020
• Titel Quelle: Enthalten in: Medical image analysis
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1996
• Jahr Quelle: 2019
• Band/Heft Quelle: 58(2019) Artikel-Nummer 101559, 15 Seiten
• ISSN Quelle: 1361-8423
• DOI: doi:10.1016/j.media.2019.101559
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Deep learning
• Sach-SW: Diffusion-weighted imaging
• Sach-SW: Fiber tractography
• Sach-SW: Machine learning
• K10plus-PPN: 1688925031

Abstract

While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms is time-consuming, requires expert knowledge and is hard to reproduce. In previous work we presented tract orientation mapping (TOM) as a novel concept for bundle-specific tractography. It is based on a learned mapping from the original fiber orientation distribution function (FOD) peaks to tract specific peaks, called tract orientation maps. Each tract orientation map represents the voxel-wise principal orientation of one tract. Here, we present an extension of this approach that combines TOM with accurate segmentations of the tract outline and its start and end region. We also introduce a custom probabilistic tracking algorithm that samples from a Gaussian distribution with fixed standard deviation centered on each peak thus enabling more complete trackings on the tract orientation maps than deterministic tracking. These extensions enable the automatic creation of bundle-specific tractograms with previously unseen accuracy. We show for 72 different bundles on high quality, low quality and phantom data that our approach runs faster and produces more accurate bundle-specific tractograms than 7 state of the art benchmark methods while avoiding cumbersome processing steps like whole brain tractography, non-linear registration, clustering or manual dissection. Moreover, we show on 17 datasets that our approach generalizes well to datasets acquired with different scanners and settings as well as with pathologies. The code of our method is openly available at https://github.com/MIC-DKFZ/TractSeg.