Abstract
Automated segmentation of glioma and its subregions has significant importance throughout the clinical work flow including diagnosis, monitoring and treatment planning of brain cancer. The automatic delineation of tumours have draw much attention in the past few years, particularly the neural network based supervised learning methods. While the clinical data acquisition is much expensive and time consuming, which is the key limitation of machine learning in medical data. We describe a solution for the brain tumor segmentation in the context of the BRATS19 challenge. The major learning scheme is based on the 3D-Unet encoder and decoder with intense data augmentation followed by bias correction. At the moment we submit this short paper, our solution achieved Dice scores of 76.84, 85.74 and 74.51 for the enhancing tumor, whole tumor and tumor core, respectively on the validation data.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Grant No. 61605014) and the Fundamental Research Funds for the Central Universities (Grant No. 2018RC17 and 2018RC18).
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Yin, P., Hu, Y., Liu, J., Duan, J., Yang, W., Cheng, K. (2020). The Tumor Mix-Up in 3D Unet for Glioma Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_26
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DOI: https://doi.org/10.1007/978-3-030-46643-5_26
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