@inproceedings{8bda8562b74e4a0baad66eabd9443117,
title = "3D deep neural network-based brain tumor segmentation using multimodality magnetic resonance sequences",
abstract = "Brain tumor segmentation plays a pivotal role in clinical practice and research settings. In this paper, we propose a 3D deep neural network-based algorithm for joint brain tumor detection and intra-tumor structure segmentation, including necrosis, edema, non-enhancing and enhancing tumor, using multimodal magnetic resonance imaging sequences. An ensemble of cascaded U-Nets is designed to detect the tumor and a deep convolutional neural network is constructed for patch-based intra-tumor structure segmentation. This algorithm has been evaluated on the BraTS 2017 Challenge dataset and achieved Dice similarity coefficients of 0.81, 0.69 and 0.55 in the segmentation of whole tumor, core tumor and enhancing tumor, respectively. Our results suggest that the proposed algorithm has promising performance in automated brain tumor segmentation.",
keywords = "Brain tumor segmentation, Cascaded U-Nets, Deep convolutional neural network, Deep learning, Magnetic Resonance Imaging (MRI)",
author = "Yan Hu and Yong Xia",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017 ; Conference date: 14-09-2017 Through 14-09-2017",
year = "2018",
doi = "10.1007/978-3-319-75238-9\_36",
language = "英语",
isbn = "9783319752372",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "423--434",
editor = "Bjoern Menze and Alessandro Crimi and Hugo Kuijf and Mauricio Reyes and Spyridon Bakas",
booktitle = "Brainlesion",
}