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

Publication date: Available online 9 August 2018Source: NeuroImageAuthor(s): Aaron Carass, Jennifer L. Cuzzocreo, Shuo Han, Carlos R. Hernandez-Castillo, Paul E. Rasser, Melanie Ganz, Vincent Beliveau, Jose Dolz, Ismail Ben Ayed, Christian Desrosiers, Benjamin Thyreau, José E. Romero, Pierrick Coupé, José V. Manjón, Vladimir S. Fonov, D. Louis Collins, Sarah H. Ying, Chiadi U. Onyike, Deana Crocetti, Bennett A. LandmanAbstractThe 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 alg...

Source: NeuroImage - August 10, 2018 Category: Neuroscience Source Type: research