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Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Carass, A, Cuzzocreo, JL, Han, S, Hernandez-Castillo, CR, Rasser, PE, Ganz, M, Beliveau, V, Dolz, J, Ben Ayed, I, Desrosiers, C, Thyreau, B, Romero, JE, Coupé, P, Manjón, JV, Fonov, VS, Collins, DL, Ying, SH, Onyike, CU, Crocetti, D, Landman, BA, Mostofsky, SH, Thompson, PM & Prince, JL 2018, 'Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images' NeuroImage, vol. 183, pp. 150-172. https://doi.org/10.1016/j.neuroimage.2018.08.003

APA

Carass, A., Cuzzocreo, J. L., Han, S., Hernandez-Castillo, C. R., Rasser, P. E., Ganz, M., ... Prince, J. L. (2018). Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. NeuroImage, 183, 150-172. https://doi.org/10.1016/j.neuroimage.2018.08.003

CBE

Carass A, Cuzzocreo JL, Han S, Hernandez-Castillo CR, Rasser PE, Ganz M, Beliveau V, Dolz J, Ben Ayed I, Desrosiers C, Thyreau B, Romero JE, Coupé P, Manjón JV, Fonov VS, Collins DL, Ying SH, Onyike CU, Crocetti D, Landman BA, Mostofsky SH, Thompson PM, Prince JL. 2018. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. NeuroImage. 183:150-172. https://doi.org/10.1016/j.neuroimage.2018.08.003

MLA

Vancouver

Author

Carass, Aaron ; Cuzzocreo, Jennifer L ; Han, Shuo ; Hernandez-Castillo, Carlos R ; Rasser, Paul E ; Ganz, Melanie ; Beliveau, Vincent ; Dolz, Jose ; Ben Ayed, Ismail ; Desrosiers, Christian ; Thyreau, Benjamin ; Romero, José E ; Coupé, Pierrick ; Manjón, José V ; Fonov, Vladimir S ; Collins, D Louis ; Ying, Sarah H ; Onyike, Chiadi U ; Crocetti, Deana ; Landman, Bennett A ; Mostofsky, Stewart H ; Thompson, Paul M ; Prince, Jerry L. / Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. In: NeuroImage. 2018 ; Vol. 183. pp. 150-172.

Bibtex

@article{1be391ccbdc1469e8d8bb8ca5e88df88,
title = "Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images",
abstract = "The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.",
author = "Aaron Carass and Cuzzocreo, {Jennifer L} and Shuo Han and Hernandez-Castillo, {Carlos R} and Rasser, {Paul E} and Melanie Ganz and Vincent Beliveau and Jose Dolz and {Ben Ayed}, Ismail and Christian Desrosiers and Benjamin Thyreau and Romero, {Jos{\'e} E} and Pierrick Coup{\'e} and Manj{\'o}n, {Jos{\'e} V} and Fonov, {Vladimir S} and Collins, {D Louis} and Ying, {Sarah H} and Onyike, {Chiadi U} and Deana Crocetti and Landman, {Bennett A} and Mostofsky, {Stewart H} and Thompson, {Paul M} and Prince, {Jerry L}",
note = "Copyright {\circledC} 2018 Elsevier Inc. All rights reserved.",
year = "2018",
month = "12",
doi = "10.1016/j.neuroimage.2018.08.003",
language = "English",
volume = "183",
pages = "150--172",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press",

}

RIS

TY - JOUR

T1 - Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

AU - Carass, Aaron

AU - Cuzzocreo, Jennifer L

AU - Han, Shuo

AU - Hernandez-Castillo, Carlos R

AU - Rasser, Paul E

AU - Ganz, Melanie

AU - Beliveau, Vincent

AU - Dolz, Jose

AU - Ben Ayed, Ismail

AU - Desrosiers, Christian

AU - Thyreau, Benjamin

AU - Romero, José E

AU - Coupé, Pierrick

AU - Manjón, José V

AU - Fonov, Vladimir S

AU - Collins, D Louis

AU - Ying, Sarah H

AU - Onyike, Chiadi U

AU - Crocetti, Deana

AU - Landman, Bennett A

AU - Mostofsky, Stewart H

AU - Thompson, Paul M

AU - Prince, Jerry L

N1 - Copyright © 2018 Elsevier Inc. All rights reserved.

PY - 2018/12

Y1 - 2018/12

N2 - The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

AB - The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

U2 - 10.1016/j.neuroimage.2018.08.003

DO - 10.1016/j.neuroimage.2018.08.003

M3 - Journal article

VL - 183

SP - 150

EP - 172

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -

ID: 55237200