An open-source tool for longitudinal whole-brain and white matter lesion segmentation
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
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.
I: NeuroImage: Clinical, Bind 38, 103354, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
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
N1 - Publisher Copyright: © 2023
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