Abstract
In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H\(^2\)NF-Net) to segment brain tumor in multimodal MR images. Our H\(^2\)NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions and combines the predictions together as the final segmentation. We trained and evaluated our model on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. The results on the test set show that the combination of the single and cascaded models achieved average Dice scores of 0.78751, 0.91290, and 0.85461, as well as Hausdorff distances (\(95\%\)) of 26.57525, 4.18426, and 4.97162 for the enhancing tumor, whole tumor, and tumor core, respectively. Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants.
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Acknowledgement
Haozhe Jia and Yong Xia were partially supported by the Science and Technology Innovation Committee of Shenzhen Municipality, China under Grant JCYJ20180306171334997, the National Natural Science Foundation of China under Grant 61771397, and the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under Grant CX202042. The authors would like to appreciate the efforts devoted to collect and share the BraTS 2020 dataset.
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Jia, H., Cai, W., Huang, H., Xia, Y. (2021). H\(^2\)NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task. 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_6
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