Derivation of high-resolution MRI atlases of the human cerebellum at 3T and segmentation using multiple automatically generated templates

• Published in: NeuroImage (Orlando, Fla.) Vol. 95; pp. 217 - 231
• Main Authors: Park, Min Tae M., Pipitone, Jon, Baer, Lawrence H., Winterburn, Julie L., Shah, Yashvi, Chavez, Sofia, Schira, Mark M., Lobaugh, Nancy J., Lerch, Jason P., Voineskos, Aristotle N., Chakravarty, M. Mallar
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.07.2014; Elsevier Limited
• Subjects: Adult; Algorithms; Anatomy, Artistic - methods; Atlases as Topic; Automatic; Automation; Brain Mapping; Brain research; Cerebellum; Cerebellum - anatomy & histology; Female; High resolution; Humans; Image Processing, Computer-Assisted - methods; Lobule; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medical research; Mental health; Methods; MRI; Neural networks; Schizophrenia; Studies; Cerebellum; Atlas; High resolution; MRI; Lobule; Automatic
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.03.037

The cerebellum has classically been linked to motor learning and coordination. However, there is renewed interest in the role of the cerebellum in non-motor functions such as cognition and in the context of different neuropsychiatric disorders. The contribution of neuroimaging studies to advancing understanding of cerebellar structure and function has been limited, partly due to the cerebellum being understudied as a result of contrast and resolution limitations of standard structural magnetic resonance images (MRI). These limitations inhibit proper visualization of the highly compact and detailed cerebellar foliations. In addition, there is a lack of robust algorithms that automatically and reliably identify the cerebellum and its subregions, further complicating the design of large-scale studies of the cerebellum. As such, automated segmentation of the cerebellar lobules would allow detailed population studies of the cerebellum and its subregions. In this manuscript, we describe a novel set of high-resolution in vivo atlases of the cerebellum developed by pairing MR imaging with a carefully validated manual segmentation protocol. Using these cerebellar atlases as inputs, we validate a novel automated segmentation algorithm that takes advantage of the neuroanatomical variability that exists in a given population under study in order to automatically identify the cerebellum, and its lobules. Our automatic segmentation results demonstrate good accuracy in the identification of all lobules (mean Kappa [κ]=0.731; range 0.40–0.89), and the entire cerebellum (mean κ=0.925; range 0.90–0.94) when compared to “gold-standard” manual segmentations. These results compare favorably in comparison to other publically available methods for automatic segmentation of the cerebellum. The completed cerebellar atlases are available freely online (http://imaging-genetics.camh.ca/cerebellum) and can be customized to the unique neuroanatomy of different subjects using the proposed segmentation pipeline (https://github.com/pipitone/MAGeTbrain). •High resolution MRI atlases of the human cerebellum were developed at 3T.•The segmentation protocol performs very well in reliability assessments.•Atlases were used for automatic segmentation within the MAGeT Brain framework.•MAGeT Brain produced accurate segmentations comparable to previous methods.•The full atlases and the code for MAGeT Brain are publicly available online.