Abstract
In our previous work, i.e., HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging. In this paper, we extend our HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks to further exploit global semantic discrimination for the obtained high-resolution features. We trained and evaluated our HNF-Netv2 on the multi-modal Brain Tumor Segmentation Challenge (BraTS) 2021 dataset. The result on the test set shows that our HNF-Netv2 achieved the average Dice scores of 0.878514, 0.872985, and 0.924919, as well as the Hausdorff distances (\(95\%\)) of 8.9184, 16.2530, and 4.4895 for the enhancing tumor, tumor core, and whole tumor, respectively. Our method won the RSNA 2021 Brain Tumor AI Challenge Prize (Segmentation Task), which ranks 8th out of all 1250 submitted results.
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Acknowledgement
Haozhe Jia, Chao Bai, 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.
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Jia, H., Bai, C., Cai, W., Huang, H., Xia, Y. (2022). HNF-Netv2 for Brain Tumor Segmentation Using Multi-modal MR Imaging. 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_10
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