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

Shuo Han, Aaron Carass, Yufan He, Jerry L. Prince

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number116819
JournalNeuroImage
Volume218
DOIs
StatePublished - Sep 2020

Keywords

  • Cerebellum
  • Convolutional neural networks
  • Parcellation

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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