@inproceedings{782c8e4fc3c04913a78f263b4f40611f,
title = "Automatic brain tumor segmentation with domain adaptation",
abstract = "Deep convolution neural networks, in particular, the encoder-decoder networks, have been extensively used in image segmentation. We develop a deep learning approach for tumor segmentation by combining a modified U-Net and its domain-adapted version (DAU-Net). We divide training samples into two domains according to preliminary segmentation results, and then equip the modified U-Net with domain adaptation structure to obtain a domain invariant feature representation. Our proposed segmentation approach is applied to the BraTS 2018 challenge for brain tumor segmentation, and achieves the mean dice score of 0.91044, 0.85057 and 0.80536 for whole tumor, tumor core and enhancing tumor, respectively, on the challenge{\textquoteright}s validation data set.",
keywords = "Brain tumor, Confusion loss, Domain adaptation, Encoder-decoder network, Segmentation",
author = "Lutao Dai and Tengfei Li and Hai Shu and Liming Zhong and Haipeng Shen and Hongtu Zhu",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",
year = "2019",
doi = "10.1007/978-3-030-11726-9_34",
language = "English (US)",
isbn = "9783030117252",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "380--392",
editor = "Mauricio Reyes and Spyridon Bakas and {van Walsum}, Theo and Alessandro Crimi and Farahani Keyvan and Hugo Kuijf",
booktitle = "Brainlesion",
}