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

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 in...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 183; S. 150 - 172
Hauptverfasser:	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.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.12.2018 Elsevier Limited Elsevier
Schlagworte:	Adult Algorithms Alzheimer's disease Attention Deficit Disorder with Hyperactivity - diagnostic imaging Attention deficit hyperactivity disorder Autism Autism Spectrum Disorder - diagnostic imaging Automation Cerebellar ataxia Cerebellar Ataxia - diagnostic imaging Cerebellum Cerebellum - diagnostic imaging Child Cognitive ability Cohort Studies Computer Science Dementia Female Humans Image Processing, Computer-Assisted - methods Image Processing, Computer-Assisted - standards Learning algorithms Machine Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Magnetic Resonance Imaging - standards Male Medical Imaging Mental disorders Motor task performance Neuroimaging - methods Neuroimaging - standards Neurological diseases Pediatrics Schizophrenia Short term memory Teams Autism Magnetic resonance imaging Cerebellar ataxia Attention deficit hyperactivity disorder
ISSN:	1053-8119, 1095-9572, 1095-9572
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Zusammenfassung:	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. •First paper to evaluate the state-of-the-art in cerebellum parcellation.•Presenting results on both Adult and Pediatric Cohorts.•Adult Cohort contains healthy controls, and patients with either symptoms of cerebellar dysfunction or SCA 6.•Pediatric Cohort contains healthy controls, and patients with ADHD or Autism.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 PMCID: PMC6271471 These authors curated the data and organized the comparison, all others contributed results.
ISSN:	1053-8119 1095-9572 1095-9572
DOI:	10.1016/j.neuroimage.2018.08.003