@inproceedings{b55d4f5a07d64b77a70fdcd03b27a412,
title = "Segmenting brain tumors from MRI using cascaded multi-modal U-Nets",
abstract = "Gliomas are the most common primary brain tumors, and their accurate manual delineation is a time- consuming and very user-dependent process. Therefore, developing automated techniques for reproducible detection and segmentation of brain tumors from magnetic resonance imaging is a vital research topic. In this paper, we present a deep learning-powered approach for brain tumor segmentation which exploits multiple magnetic-resonance modalities and processes them in two cascaded stages. In both stages, we use multi-modal fully-convolutional neural nets inspired by U-Nets. The first stage detects regions of interests, whereas the second stage performs the multi-class classification. Our experimental study, performed over the newest release of the BraTS dataset (BraTS 2018) showed that our method delivers accurate brain-tumor delineation and offers very fast processing{\textemdash}the total time required to segment one study using our approach amounts to around 18 s.",
keywords = "Brain tumor, CNN, Deep learning, Segmentation",
author = "Michal Marcinkiewicz and Jakub Nalepa and Lorenzo, \{Pablo Ribalta\} and Wojciech Dudzik and Grzegorz Mrukwa",
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\_2",
language = "English",
isbn = "9783030117252",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "13--24",
editor = "Alessandro Crimi and Spyridon Bakas and Hugo Kuijf and Farahani Keyvan and Mauricio Reyes and \{van Walsum\}, Theo",
booktitle = "Brainlesion",
address = "Germany",
}