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 ground-truth of the dataset using watershedding, and several strategies for data augmentation that allowed to train robustly the U-Net to perform the segmentation. Finally, we deployed the trained network freely available.
S. Pontes-Filho and A. G. Dahl contributed equally to this work.
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Notes
- 1.
Tool is available at https://github.com/sidneyp/skull-stripper.
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Acknowledgment
This work was supported by Norwegian Research Council SOCRATES project (grant number 270961) and received internal support as a lighthouse project in Computer Vision from the Faculty of Technology, Art and Design (TKD) at Oslo Metropolitan University, Norway.
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Pontes-Filho, S., Dahl, A.G., Nichele, S., Mello, G.B.M.e. (2022). A Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodents. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_36
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