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.
PY - 2018
Y1 - 2018
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.
KW - Attention deficit hyperactivity disorder
KW - Autism
KW - Cerebellar ataxia
KW - Magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=85051711072&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2018.08.003
DO - 10.1016/j.neuroimage.2018.08.003
M3 - Journal article
C2 - 30099076
AN - SCOPUS:85051711072
SN - 1053-8119
VL - 183
SP - 150
EP - 172
JO - NeuroImage
JF - NeuroImage
ER -
