Abstract
Automatic segmentation of brain tumor in magnetic resonance images (MRI) is necessary for diagnosis, monitoring and treatment. Manual segmentation is time-consuming, expensive and subjective. In this paper we present a robust automatic segmentation algorithm based on 3D U-Net. We propose a novel residual block with dilated convolution (res_dil block) and incorporate deep supervision to improve the segmentation results. We also compare the effect of different losses on the class imbalance problem. To prove the effectiveness of our method, we analyze each component proposed in the network architecture and we demonstrate that segmentation results can be improved by these components. Experiment results on the BraTS 2017 and BraTS 2018 datasets show that the proposed method can achieve good performance on brain tumor segmentation.
Keywords
Supported by the Normandie Regional Council via the MoNoMaD project (Grant number: 18P03397/18E01937).
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Zhou, T., Ruan, S., Hu, H., Canu, S. (2019). Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_66
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DOI: https://doi.org/10.1007/978-3-030-32692-0_66
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