A deep learning based tool for automatic brain extraction from functional magnetic resonance images in rodents

TL;DR

This paper introduces a U-Net based deep learning tool for automatic brain extraction from rodent fMRI images, addressing the limitations of human-focused tools and speeding up data preprocessing.

Contribution

The study develops a novel neural network approach tailored for rodent fMRI data, with strategies for rapid training data generation and data augmentation.

Findings

Effective brain segmentation achieved with U-Net architecture.

Training speed improved through watershedding and data augmentation.

The trained model is freely available for use.

Abstract

Removing skull artifacts from functional magnetic images (fMRI) is a well understood and frequently encountered problem. Because the fMRI field has grown mostly due to human studies, many new tools were developed to handle human data. Nonetheless, these tools are not equally useful to handle the data derived from animal studies, especially from rodents. This represents a major problem to the field because rodent studies generate larger datasets from larger populations, which implies that preprocessing these images manually to remove the skull becomes a bottleneck in the data analysis pipeline. In this study, we address this problem by implementing a neural network based method that uses a U-Net architecture to segment the brain area into a mask and removing the skull and other tissues from the image. We demonstrate several strategies to speed up the process of generating the training dataset using watershedding and several strategies for data augmentation that allowed to train faster the U-Net to perform the segmentation. Finally, we deployed the trained network freely available.