Addressing the path-length-dependency confound in white matter tract segmentation

Research output: Contribution to journalJournal articlepeer-review

Standard

Addressing the path-length-dependency confound in white matter tract segmentation. / Liptrot, Matthew George; Sidaros, Karam; Dyrby, Tim B.

In: PLoS ONE, Vol. 9, No. 5, e96247, 2014.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Liptrot, MG, Sidaros, K & Dyrby, TB 2014, 'Addressing the path-length-dependency confound in white matter tract segmentation', PLoS ONE, vol. 9, no. 5, e96247. https://doi.org/10.1371/journal.pone.0096247

APA

Liptrot, M. G., Sidaros, K., & Dyrby, T. B. (2014). Addressing the path-length-dependency confound in white matter tract segmentation. PLoS ONE, 9(5), [e96247]. https://doi.org/10.1371/journal.pone.0096247

Vancouver

Liptrot MG, Sidaros K, Dyrby TB. Addressing the path-length-dependency confound in white matter tract segmentation. PLoS ONE. 2014;9(5). e96247. https://doi.org/10.1371/journal.pone.0096247

Author

Liptrot, Matthew George ; Sidaros, Karam ; Dyrby, Tim B. / Addressing the path-length-dependency confound in white matter tract segmentation. In: PLoS ONE. 2014 ; Vol. 9, No. 5.

Bibtex

@article{9a5bfe9a6e4247a796b2640354e16284,
title = "Addressing the path-length-dependency confound in white matter tract segmentation",
abstract = "We derive the Iterative Confidence Enhancement of Tractography (ICE-T) framework to address the problem of path-length dependency (PLD), the streamline dispersivity confound inherent to probabilistic tractography methods. We show that PLD can arise as a non-linear effect, compounded by tissue complexity, and therefore cannot be handled using linear correction methods. ICE-T is an easy-to-implement framework that acts as a wrapper around most probabilistic streamline tractography methods, iteratively growing the tractography seed regions. Tract networks segmented with ICE-T can subsequently be delineated with a global threshold, even from a single-voxel seed. We investigated ICE-T performance using ex vivo pig-brain datasets where true positives were known via in vivo tracers, and applied the derived ICE-T parameters to a human in vivo dataset. We examined the parameter space of ICE-T: the number of streamlines emitted per voxel, and a threshold applied at each iteration. As few as 20 streamlines per seed-voxel, and a robust range of ICE-T thresholds, were shown to sufficiently segment the desired tract network. Outside this range, the tract network either approximated the complete white-matter compartment (too low threshold) or failed to propagate through complex regions (too high threshold). The parameters were shown to be generalizable across seed regions. With ICE-T, the degree of both near-seed flare due to false positives, and of distal false negatives, are decreased when compared with thresholded probabilistic tractography without ICE-T. Since ICE-T only addresses PLD, the degree of remaining false-positives and false-negatives will consequently be mainly attributable to the particular tractography method employed. Given the benefits offered by ICE-T, we would suggest that future studies consider this or a similar approach when using tractography to provide tract segmentations for tract based analysis, or for brain network analysis.",
keywords = "Faculty of Science, MRI, Tractography, Path-length dependency, White matter, Brain, Segmentation, Diffusion Magnetic Resonance Imaging, DWI",
author = "Liptrot, {Matthew George} and Karam Sidaros and Dyrby, {Tim B.}",
year = "2014",
doi = "10.1371/journal.pone.0096247",
language = "English",
volume = "9",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "5",

}

RIS

TY - JOUR

T1 - Addressing the path-length-dependency confound in white matter tract segmentation

AU - Liptrot, Matthew George

AU - Sidaros, Karam

AU - Dyrby, Tim B.

PY - 2014

Y1 - 2014

N2 - We derive the Iterative Confidence Enhancement of Tractography (ICE-T) framework to address the problem of path-length dependency (PLD), the streamline dispersivity confound inherent to probabilistic tractography methods. We show that PLD can arise as a non-linear effect, compounded by tissue complexity, and therefore cannot be handled using linear correction methods. ICE-T is an easy-to-implement framework that acts as a wrapper around most probabilistic streamline tractography methods, iteratively growing the tractography seed regions. Tract networks segmented with ICE-T can subsequently be delineated with a global threshold, even from a single-voxel seed. We investigated ICE-T performance using ex vivo pig-brain datasets where true positives were known via in vivo tracers, and applied the derived ICE-T parameters to a human in vivo dataset. We examined the parameter space of ICE-T: the number of streamlines emitted per voxel, and a threshold applied at each iteration. As few as 20 streamlines per seed-voxel, and a robust range of ICE-T thresholds, were shown to sufficiently segment the desired tract network. Outside this range, the tract network either approximated the complete white-matter compartment (too low threshold) or failed to propagate through complex regions (too high threshold). The parameters were shown to be generalizable across seed regions. With ICE-T, the degree of both near-seed flare due to false positives, and of distal false negatives, are decreased when compared with thresholded probabilistic tractography without ICE-T. Since ICE-T only addresses PLD, the degree of remaining false-positives and false-negatives will consequently be mainly attributable to the particular tractography method employed. Given the benefits offered by ICE-T, we would suggest that future studies consider this or a similar approach when using tractography to provide tract segmentations for tract based analysis, or for brain network analysis.

AB - We derive the Iterative Confidence Enhancement of Tractography (ICE-T) framework to address the problem of path-length dependency (PLD), the streamline dispersivity confound inherent to probabilistic tractography methods. We show that PLD can arise as a non-linear effect, compounded by tissue complexity, and therefore cannot be handled using linear correction methods. ICE-T is an easy-to-implement framework that acts as a wrapper around most probabilistic streamline tractography methods, iteratively growing the tractography seed regions. Tract networks segmented with ICE-T can subsequently be delineated with a global threshold, even from a single-voxel seed. We investigated ICE-T performance using ex vivo pig-brain datasets where true positives were known via in vivo tracers, and applied the derived ICE-T parameters to a human in vivo dataset. We examined the parameter space of ICE-T: the number of streamlines emitted per voxel, and a threshold applied at each iteration. As few as 20 streamlines per seed-voxel, and a robust range of ICE-T thresholds, were shown to sufficiently segment the desired tract network. Outside this range, the tract network either approximated the complete white-matter compartment (too low threshold) or failed to propagate through complex regions (too high threshold). The parameters were shown to be generalizable across seed regions. With ICE-T, the degree of both near-seed flare due to false positives, and of distal false negatives, are decreased when compared with thresholded probabilistic tractography without ICE-T. Since ICE-T only addresses PLD, the degree of remaining false-positives and false-negatives will consequently be mainly attributable to the particular tractography method employed. Given the benefits offered by ICE-T, we would suggest that future studies consider this or a similar approach when using tractography to provide tract segmentations for tract based analysis, or for brain network analysis.

KW - Faculty of Science

KW - MRI

KW - Tractography

KW - Path-length dependency

KW - White matter

KW - Brain

KW - Segmentation

KW - Diffusion Magnetic Resonance Imaging

KW - DWI

U2 - 10.1371/journal.pone.0096247

DO - 10.1371/journal.pone.0096247

M3 - Journal article

C2 - 24797510

VL - 9

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 5

M1 - e96247

ER -

ID: 117138196