Automatic Brain Extraction for Rodent MRI Images

Yikang Liu, Hayreddin Said Unsal, Yi Tao, Nanyin Zhang

Research output: Contribution to journalArticle

Abstract

Rodent models are increasingly important in translational neuroimaging research. In rodent neuroimaging, particularly magnetic resonance imaging (MRI) studies, brain extraction is a critical data preprocessing component. Current brain extraction methods for rodent MRI usually require manual adjustment of input parameters due to widely different image qualities and/or contrasts. Here we propose a novel method, termed SHape descriptor selected Extremal Regions after Morphologically filtering (SHERM), which only requires a brain template mask as the input and is capable of automatically and reliably extracting the brain tissue in both rat and mouse MRI images. The method identifies a set of brain mask candidates, extracted from MRI images morphologically opened and closed sequentially with multiple kernel sizes, that match the shape of the brain template. These brain mask candidates are then merged to generate the brain mask. This method, along with four other state-of-the-art rodent brain extraction methods, were benchmarked on four separate datasets including both rat and mouse MRI images. Without involving any parameter tuning, our method performed comparably to the other four methods on all datasets, and its performance was robust with stably high true positive rates and low false positive rates. Taken together, this study provides a reliable automatic brain extraction method that can contribute to the establishment of automatic pipelines for rodent neuroimaging data analysis.

Original languageEnglish (US)
JournalNeuroinformatics
DOIs
StateAccepted/In press - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Neuroscience(all)
  • Information Systems

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