Training shortest-path tractography: automatic learning of spatial priors

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Standard

Training shortest-path tractography : automatic learning of spatial priors. / Kasenburg, Niklas; Liptrot, Matthew George; Reislev, Nina Linde; Ørting, Silas Nyboe; Nielsen, Mads; Garde, Ellen; Feragen, Aasa.

I: NeuroImage, Bind 130, 2016, s. 63-76.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Kasenburg, N, Liptrot, MG, Reislev, NL, Ørting, SN, Nielsen, M, Garde, E & Feragen, A 2016, 'Training shortest-path tractography: automatic learning of spatial priors', NeuroImage, bind 130, s. 63-76. https://doi.org/10.1016/j.neuroimage.2016.01.031

APA

Kasenburg, N., Liptrot, M. G., Reislev, N. L., Ørting, S. N., Nielsen, M., Garde, E., & Feragen, A. (2016). Training shortest-path tractography: automatic learning of spatial priors. NeuroImage, 130, 63-76. https://doi.org/10.1016/j.neuroimage.2016.01.031

Vancouver

Kasenburg N, Liptrot MG, Reislev NL, Ørting SN, Nielsen M, Garde E o.a. Training shortest-path tractography: automatic learning of spatial priors. NeuroImage. 2016;130:63-76. https://doi.org/10.1016/j.neuroimage.2016.01.031

Author

Kasenburg, Niklas ; Liptrot, Matthew George ; Reislev, Nina Linde ; Ørting, Silas Nyboe ; Nielsen, Mads ; Garde, Ellen ; Feragen, Aasa. / Training shortest-path tractography : automatic learning of spatial priors. I: NeuroImage. 2016 ; Bind 130. s. 63-76.

Bibtex

@article{085f171a89a943638f52b7cc2d89f313,
title = "Training shortest-path tractography: automatic learning of spatial priors",
abstract = "Abstract Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we demonstrate that our framework also retains support for conventional interactive constraints such as waypoint regions. We apply our approach to the open access, high quality Human Connectome Project data, as well as a dataset acquired on a typical clinical scanner. Our results show that the use of a learned prior substantially increases the overlap of tractography output with a reference atlas on both populations, and this is confirmed by visual inspection. Furthermore, we demonstrate how a prior learned on the high quality dataset significantly increases the overlap with the reference for the more typical yet lower quality data acquired on a clinical scanner. We hope that such automatic incorporation of prior knowledge and the obviation of expert interactive tract delineation on every subject, will improve the feasibility of large clinical tractography studies.",
keywords = "Prior information",
author = "Niklas Kasenburg and Liptrot, {Matthew George} and Reislev, {Nina Linde} and {\O}rting, {Silas Nyboe} and Mads Nielsen and Ellen Garde and Aasa Feragen",
year = "2016",
doi = "10.1016/j.neuroimage.2016.01.031",
language = "English",
volume = "130",
pages = "63--76",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Training shortest-path tractography

T2 - automatic learning of spatial priors

AU - Kasenburg, Niklas

AU - Liptrot, Matthew George

AU - Reislev, Nina Linde

AU - Ørting, Silas Nyboe

AU - Nielsen, Mads

AU - Garde, Ellen

AU - Feragen, Aasa

PY - 2016

Y1 - 2016

N2 - Abstract Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we demonstrate that our framework also retains support for conventional interactive constraints such as waypoint regions. We apply our approach to the open access, high quality Human Connectome Project data, as well as a dataset acquired on a typical clinical scanner. Our results show that the use of a learned prior substantially increases the overlap of tractography output with a reference atlas on both populations, and this is confirmed by visual inspection. Furthermore, we demonstrate how a prior learned on the high quality dataset significantly increases the overlap with the reference for the more typical yet lower quality data acquired on a clinical scanner. We hope that such automatic incorporation of prior knowledge and the obviation of expert interactive tract delineation on every subject, will improve the feasibility of large clinical tractography studies.

AB - Abstract Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we demonstrate that our framework also retains support for conventional interactive constraints such as waypoint regions. We apply our approach to the open access, high quality Human Connectome Project data, as well as a dataset acquired on a typical clinical scanner. Our results show that the use of a learned prior substantially increases the overlap of tractography output with a reference atlas on both populations, and this is confirmed by visual inspection. Furthermore, we demonstrate how a prior learned on the high quality dataset significantly increases the overlap with the reference for the more typical yet lower quality data acquired on a clinical scanner. We hope that such automatic incorporation of prior knowledge and the obviation of expert interactive tract delineation on every subject, will improve the feasibility of large clinical tractography studies.

KW - Prior information

U2 - 10.1016/j.neuroimage.2016.01.031

DO - 10.1016/j.neuroimage.2016.01.031

M3 - Journal article

C2 - 26804779

VL - 130

SP - 63

EP - 76

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -

ID: 160610559