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dc.contributor.authorGulden Dahl, Annelene
dc.contributor.authorNichele, Stefano
dc.contributor.authorMello, Gustavo
dc.date.accessioned2022-02-16T14:07:20Z
dc.date.available2022-02-16T14:07:20Z
dc.date.created2021-12-01T11:29:58Z
dc.date.issued2021
dc.identifier.isbn978-3-030-82195-1
dc.identifier.urihttps://hdl.handle.net/11250/2979451
dc.description.abstractRemoving 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.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofIntelligent Systems and Applications, Proceedings of the 2021 Intelligent Systems Conference (IntelliSys 2021) Volume 1
dc.titleA Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodentsen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.rights.holderThis is the authors' accepted manuscript to a chapter published by Springer. Locked until 7.8.2022 due to copyright restrictions.en_US
dc.identifier.doi10.1007/978-3-030-82199-9_36
dc.identifier.cristin1962536
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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