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2021 | OriginalPaper | Chapter

Lightweight U-Nets for Brain Tumor Segmentation

Authors : Tomasz Tarasiewicz, Michal Kawulok, Jakub Nalepa

Published in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Publisher: Springer International Publishing

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Abstract

Automated brain tumor segmentation is a vital topic due to its clinical applications. We propose to exploit a lightweight U-Net-based deep architecture called Skinny for this task—it was originally employed for skin detection from color images, and benefits from a wider spatial context. We train multiple Skinny networks over all image planes (axial, coronal, and sagittal), and form an ensemble containing such models. The experiments showed that our approach allows us to obtain accurate brain tumor delineation from multi-modal magnetic resonance images.

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next chapter Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised Learners

Our team is named ttarasiewicz.

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Title: Lightweight U-Nets for Brain Tumor Segmentation
Authors: Tomasz Tarasiewicz
Michal Kawulok
Jakub Nalepa
Publisher: Springer International Publishing
Book: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Print ISBN: 978-3-030-72086-5

Electronic ISBN: 978-3-030-72087-2

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-72087-2_1

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