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Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization.

ABSTRACT: The cerebellum plays a central role in sensory input, voluntary motor action, and many neuropsychological functions and is involved in many brain diseases and neurological disorders. Cerebellar parcellation from magnetic resonance images provides a way to study regional cerebellar atrophy and also provides an anatomical map for functional imaging. In a recent comparison, a multi-atlas approach proved to be superior to other parcellation methods including some based on convolutional neural networks (CNNs) which have a considerable speed advantage. In this work, we developed an alternative CNN design for cerebellar parcellation, yielding a method that achieves the leading performance to date. The proposed method was evaluated on multiple data sets to show its broad applicability, and a Singularity container has been made publicly available.

SUBMITTER: Han S

PROVIDER: S-EPMC7416473 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization.

Han Shuo S Carass Aaron A He Yufan Y Prince Jerry L JL

NeuroImage 20200511

The cerebellum plays a central role in sensory input, voluntary motor action, and many neuropsychological functions and is involved in many brain diseases and neurological disorders. Cerebellar parcellation from magnetic resonance images provides a way to study regional cerebellar atrophy and also provides an anatomical map for functional imaging. In a recent comparison, a multi-atlas approach proved to be superior to other parcellation methods including some based on convolutional neural networ ...[more]

PMID: 32438049

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