Example image of brain tissue extracted by BEN, showing the ‘raw’ or unprocessed image on the left, and the part of the image that BEN extracts on the right. Image credit: Ziqi Yu
Magnetic resonance imaging (MRI) is an ideal way to obtain high-resolution images of the whole brain of rodents and primates (including humans) non-invasively. A critical step in processing MRI data is brain tissue extraction, which consists on removing the signal from the non-neural tissues around the brain, such as the skull or fat, from the images. If this step is done incorrectly, it can lead to images with signals that do not correspond to the brain, which can compromise downstream analysis, and lead to errors when comparing samples from similar species. Although several traditional toolboxes to perform brain extraction are available, most of them focus on human brains, and no standardized methods are available for other mammals, such as rodents and monkeys.
To bridge this gap, Yu et al. developed a computational method based on deep learning (a type of machine learning that imitates how humans learn certain types of information) named the Brain Extraction Net (BEN). BEN can extract brain tissues across species, MRI modalities, and scanners to provide a generalizable toolbox for neuroimaging using MRI. Next, Yu et al. demonstrated BEN’s functionality in a large-scale experiment involving brain tissue extraction in eighteen different MRI datasets from different species. In these experiments, BEN was shown to improve the robustness and accuracy of processing brain magnetic resonance imaging data.
Brain tissue extraction is essential for MRI-based neuroimaging studies, so BEN can benefit both the neuroimaging and the neuroscience communities. Importantly, the tool is an open-source software, allowing other researchers to use it freely. Additionally, it is an extensible tool that allows users to provide their own data and pre-trained networks to further improve BEN’s generalization. Yu et al. have also designed interfaces to support other popular neuroimaging processing pipelines and to directly deal with external datasets, enabling scientists to use it to extract brain tissue in their own experiments.
