An open-source tool for longitudinal whole-brain and white matter lesion segmentation

Research output: Contribution to journal › Journal article › Research › peer-review

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An open-source tool for longitudinal whole-brain and white matter lesion segmentation. / Cerri, Stefano; Greve, Douglas N.; Hoopes, Andrew; Lundell, Henrik; Siebner, Hartwig R.; Mühlau, Mark; Van Leemput, Koen.

In: NeuroImage: Clinical, Vol. 38, 103354, 2023.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Cerri, S, Greve, DN, Hoopes, A, Lundell, H, Siebner, HR, Mühlau, M & Van Leemput, K 2023, 'An open-source tool for longitudinal whole-brain and white matter lesion segmentation', NeuroImage: Clinical, vol. 38, 103354. https://doi.org/10.1016/j.nicl.2023.103354

APA

Cerri, S., Greve, D. N., Hoopes, A., Lundell, H., Siebner, H. R., Mühlau, M., & Van Leemput, K. (2023). An open-source tool for longitudinal whole-brain and white matter lesion segmentation. NeuroImage: Clinical, 38, [103354]. https://doi.org/10.1016/j.nicl.2023.103354

Vancouver

Cerri S, Greve DN, Hoopes A, Lundell H, Siebner HR, Mühlau M et al. An open-source tool for longitudinal whole-brain and white matter lesion segmentation. NeuroImage: Clinical. 2023;38. 103354. https://doi.org/10.1016/j.nicl.2023.103354

Author

Cerri, Stefano ; Greve, Douglas N. ; Hoopes, Andrew ; Lundell, Henrik ; Siebner, Hartwig R. ; Mühlau, Mark ; Van Leemput, Koen. / An open-source tool for longitudinal whole-brain and white matter lesion segmentation. In: NeuroImage: Clinical. 2023 ; Vol. 38.

Bibtex

@article{575b3d6a7ce84a37a5adbf4135799eaa,

title = "An open-source tool for longitudinal whole-brain and white matter lesion segmentation",

abstract = "In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test–retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.",

keywords = "FreeSurfer, Generative models, Lesion segmentation, Longitudinal segmentation, Whole-brain segmentation",

author = "Stefano Cerri and Greve, {Douglas N.} and Andrew Hoopes and Henrik Lundell and Siebner, {Hartwig R.} and Mark M{\"u}hlau and {Van Leemput}, Koen",

note = "Publisher Copyright: {\textcopyright} 2023",

year = "2023",

doi = "10.1016/j.nicl.2023.103354",

language = "English",

volume = "38",

journal = "NeuroImage: Clinical",

issn = "2213-1582",

publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - An open-source tool for longitudinal whole-brain and white matter lesion segmentation

AU - Cerri, Stefano

AU - Greve, Douglas N.

AU - Hoopes, Andrew

AU - Lundell, Henrik

AU - Siebner, Hartwig R.

AU - Mühlau, Mark

AU - Van Leemput, Koen

PY - 2023

Y1 - 2023

N2 - In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test–retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.

AB - In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test–retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.

KW - FreeSurfer

KW - Generative models

KW - Lesion segmentation

KW - Longitudinal segmentation

KW - Whole-brain segmentation

U2 - 10.1016/j.nicl.2023.103354

DO - 10.1016/j.nicl.2023.103354

M3 - Journal article

C2 - 36907041

AN - SCOPUS:85149821827

VL - 38

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

SN - 2213-1582

M1 - 103354

ER -

ID: 367084050

Department of Clinical Medicine