@inproceedings{d2d0df28002e4ecfa2a433745ccc8b5a,
title = "Automatic brain structures segmentation using deep residual dilated U-Net",
abstract = "Brain image segmentation is used for visualizing and quantifying anatomical structures of the brain. We present an automated approach using 2D deep residual dilated networks which captures rich context information of different tissues for the segmentation of eight brain structures. The proposed system was evaluated in the MICCAI Brain Segmentation Challenge (http://mrbrains18.isi.uu.nl/ ) and ranked 9 th out of 22 teams. We further compared the method with traditional U-Net using leave-one-subject-out cross-validation setting on the public dataset. Experimental results shows that the proposed method outperforms traditional U-Net (i.e. 80.9% vs 78.3% in averaged Dice score, 4.35 mm vs 11.59 mm in averaged robust Hausdorff distance) and is computationally efficient.",
keywords = "Brain structure segmentation, Deep learning",
author = "Hongwei Li and Andrii Zhygallo and Bjoern Menze",
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 = "10.1007/978-3-030-11723-8_39",
language = "English",
isbn = "9783030117221",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "385--393",
editor = "Mauricio Reyes and {van Walsum}, Theo and Hugo Kuijf and Spyridon Bakas and Farahani Keyvan and Alessandro Crimi",
booktitle = "Brainlesion",
}