Abstract
Brain tumor segmentation has wide applications and important potential values for glioblastoma research. Because of the complexity of the structure of subtype tumors and the different visual scenes of multi modalities like T1, T1ce, T2, and FLAIR, most methods fail to segment the brain tumors with high accuracy. The sizes and shapes of tumors are very diverse in the wild. Another problem is that most recent algorithms ignore the multi-scale information of brain tumor features. To handle these problems, an ensemble method that utilizes the strength of dilated convolution in capturing larger receptive fields, which has more context information of brain image, also gets the ability of small tumor segmentation by using multiple tasks learning. Besides, we apply the generalized wasserstein dice loss function in training the model to solve the problem of imbalanced between multi-class segmentation. The experimental results demonstrate that the proposed ensemble method improves the accuracy in brain tumor segmentation, showing superiority to other recent segmentation methods.
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Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2018R1D1A3B05049058) and also by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) & funded by the Korean government (MSIT) (NRF 2019M3E5D1A02067961).
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Do, TBT., Trinh, DL., Tran, MT., Lee, GS., Kim, SH., Yang, HJ. (2022). Deep Learning Based Ensemble Approach for 3D MRI 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 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_19
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