@inproceedings{6331a5f571974642b9648b3034678f04,
title = "Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets",
abstract = "In this paper, we exploit a cascaded 3D U-Net architecture to perform detection and segmentation of brain tumors (low- and high-grade gliomas) from multi-modal magnetic resonance scans. First, we detect tumors in a binary-classification setting, and they later undergo multi-class segmentation. To provide high-quality generalization, we investigate several regularization techniques that help improve the segmentation performance obtained for the unseen scans, and benefit from the expert knowledge of a senior radiologist captured in a form of several post-processing routines. Our preliminary experiments elaborated over the BraTS{\textquoteright}20 validation set revealed that our approach delivers high-quality tumor delineation.",
keywords = "Brain tumor, Deep learning, Segmentation, U-Net",
author = "Krzysztof Kotowski and Szymon Adamski and Wojciech Malara and Bartosz Machura and Lukasz Zarudzki and Jakub Nalepa",
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\_23",
language = "English",
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 = "265--277",
editor = "Alessandro Crimi and Spyridon Bakas",
booktitle = "Brainlesion",
address = "Germany",
}