Journal Article

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

Collins, D. Louis, Dolz, Jose, Cuzzocreo, Jennifer L., Han, Shuo, Desrosiers, Christian, Romero, José E., Carass, Aaron, Mostofsky, Stewart H., Landman, Bennett A., Ganz, Melanie, Beliveau, Vincent, Ying, Sarah H., Crocetti, Deana, Hernandez-Castillo, Carlos R., Ben Ayed, Ismail, Thyreau, Benjamin, Manjón, José V., Fonov, Vladimir S., Rasser, Paul E., Coupé, Pierrick, Thompson, Paul M., Prince, Jerry L., Onyike, Chiadi U.

2018

Overview

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.

• Publisher: Elsevier Inc,Elsevier Limited,Elsevier
• Subject: 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