Abstract
Semantic segmentation plays an important role in the prevention, diagnosis and treatment of brain glioma. In this paper, we propose a dense channels 2D U-net segmentation model with residual unit and feature pyramid unit. The main difference compared with other U-net models is that the number of bottom feature components is increased, so that the network can learn more abundant patterns. We also develop a multiple feature extraction network model to extract rich and diverse features, which is conducive to segmentation. Finally, we employ decision tree regression model to predict patient overall survival by the different texture, shape and first-order features extracted from BraTS 2019 dataset.
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Acknowledgments
The work is supported by the Fundamental Research Funds of Shandong University (Grant No. 2017JC013), the Shandong Province Key Innovation Project (Grant No. 2017CXGC1504, 2017CXGC1502) and the Natural Science Foundation of Shandong Province (Grant No. ZR2019MH049).
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Shi, W., Pang, E., Wu, Q., Lin, F. (2020). Brain Tumor Segmentation Using Dense Channels 2D U-net and Multiple Feature Extraction Network. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_26
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DOI: https://doi.org/10.1007/978-3-030-46640-4_26
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