Abstract
3D brain tumor segmentation is essential for the diagnosis, monitoring, and treatment planning of brain diseases. In recent studies, the Deep Convolution Neural Network (DCNN) is one of the most potent methods for medical image segmentation. In this paper, we review the different kinds of tricks applied to 3D brain tumor segmentation with DNN. We divide such tricks into three main categories: data processing methods including data sampling, random patch-size training, and semi-supervised learning, model devising methods including architecture devising and result fusing, and optimizing processes including warming-up learning and multi-task learning. Most of these approaches are not particular to brain tumor segmentation, but applicable to other medical image segmentation problems as well. Evaluated on the BraTS2019 online testing set, we obtain Dice scores of 0.810, 0.883 and 0.861, and Hausdorff Distances (95th percentile) of 2.447, 4.792, and 5.581 for enhanced tumor core, whole tumor, and tumor core, respectively. Our method won the second place of the BraTS 2019 Challenge for the tumor segmentation.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China(NSFC) Grants 61773376, 61721004, 61836014, as well as Beijing Science and Technology Program Grant Z181100008918010.
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Zhao, YX., Zhang, YM., Liu, CL. (2020). Bag of Tricks for 3D MRI Brain Tumor Segmentation. 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_20
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DOI: https://doi.org/10.1007/978-3-030-46640-4_20
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