Abstract
Gliomas are brain tumors originating from the neuronal support tissue called glia, which can be benign or malignant. They are considered rare tumors, whose prognosis, which is highly fluctuating, is primarily related to several factors, including localization, size, degree of extension and certain immune factors. We propose an approach using a Residual 3D U-Net to segment these tumors with localization, a technique for centering and reducing the size of input images to make more accurate and faster predictions. We incorporated different training and post-processing techniques such as cross-validation and minimum pixel threshold.
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Acknowledgement
We would like to thank Arnaud Renard and his team, for the access to their supercomputer ROMEO, the Regional Super Computer Center hosted by the University of Reims Champagne-Ardenne. We also would like to thank Christian Chabrerie for his support throughout the project.
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Demoustier, M., Khemir, I., Nguyen, Q.D., Martin-Gaffé, L., Boutry, N. (2022). Residual 3D U-Net with Localization for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham. https://doi.org/10.1007/978-3-031-08999-2_33
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DOI: https://doi.org/10.1007/978-3-031-08999-2_33
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