@inproceedings{613a152152414b528cb8bda4e563bdbc,
title = "H2 NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task",
abstract = "In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2 NF-Net) to segment brain tumor in multimodal MR images. Our H2 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.",
keywords = "Brain tumor, Segmentation, Single and cascaded HNF-Nets",
author = "Haozhe Jia and Weidong Cai and Heng Huang and Yong Xia",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020 ; Conference date: 04-10-2020 Through 04-10-2020",
year = "2021",
doi = "10.1007/978-3-030-72087-2_6",
language = "英语",
isbn = "9783030720865",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "58--68",
editor = "Alessandro Crimi and Spyridon Bakas",
booktitle = "Brainlesion",
}