Abstract
The brain tumor segmentation is essential for diagnosis and treatment of brain diseases. However, most of current 3D deep learning technologies require large number of magnetic resonance images (MRIs). In order to make full use of small dataset like BraTS 2020, we propose a deep supervision-based 2D residual U-net for efficient and automatic brain tumor segmentation. In our network, residual blocks are used to alleviate the gradient dispersion caused by excessive depth of network, while multiple deep supervision branches are used as the regularization of the network, they can improve the training stability and enable the encoder to extract richer visual features. The CBICA’s IPP’s evaluation of the segmentation results verifies the effectiveness of our method. The average Dice of ET, WT and TC are 0.7593, 0.8726 and 0.7879 respectively.
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Ma, S., Zhang, Z., Ding, J., Li, X., Tang, J., Guo, F. (2021). A Deep Supervision CNN Network 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_14
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DOI: https://doi.org/10.1007/978-3-030-72087-2_14
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