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A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

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@article{bb8046f1550c411b947dc27969dfc8e9,
title = "A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis",
abstract = "Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.",
keywords = "Health Sciences, Lesion segmentation, multiple sclerosis, Whole-brain segmentation, Generative model",
author = "Stefano Cerri and Oula Puonti and Meier, {Dominik S} and Jens Wuerfel and Mark M{\"u}hlau and Siebner, {Hartwig R} and Leemput, {Koen Van}",
note = "Copyright {\circledC} 2020. Published by Elsevier Inc.",
year = "2021",
month = "1",
day = "15",
doi = "10.1016/j.neuroimage.2020.117471",
language = "English",
volume = "225",
pages = "1--17",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press",

}

RIS

TY - JOUR

T1 - A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

AU - Cerri, Stefano

AU - Puonti, Oula

AU - Meier, Dominik S

AU - Wuerfel, Jens

AU - Mühlau, Mark

AU - Siebner, Hartwig R

AU - Leemput, Koen Van

N1 - Copyright © 2020. Published by Elsevier Inc.

PY - 2021/1/15

Y1 - 2021/1/15

N2 - Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.

AB - Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.

KW - Health Sciences

KW - Lesion segmentation

KW - multiple sclerosis

KW - Whole-brain segmentation

KW - Generative model

U2 - 10.1016/j.neuroimage.2020.117471

DO - 10.1016/j.neuroimage.2020.117471

M3 - Journal article

VL - 225

SP - 1

EP - 17

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

M1 - 117471

ER -

ID: 61112630