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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Notes
- 1.
Our team is named ttarasiewicz.
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Acknowledgments
This research was supported by the National Science Centre, Poland, under Research Grant DEC-2017/25/B/ST6/00474. MK was supported by the Silesian University of Technology funds through the Rector’s Research and Development Grant 02/080/RGJ20/0004. JN was supported by the Silesian University of Technology grant for maintaining and developing research potential, and by the Rector’s Research and Development Grant 02/080/RGJ20/0003.
This paper is in memory of Dr. Grzegorz Nalepa, an extraordinary scientist and pediatric hematologist/oncologist at Riley Hospital for Children, Indianapolis, USA, who helped countless patients and their families through some of the most challenging moments of their lives.
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Tarasiewicz, T., Kawulok, M., Nalepa, J. (2021). Lightweight U-Nets for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_1
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