@inproceedings{fa37d04b2b4b4ed18f78e118e22869ce,
title = "Brain Tumor Segmentation on MRI with Missing Modalities",
abstract = "Brain Tumor Segmentation from magnetic resonance imaging (MRI) is a critical technique for early diagnosis. However, rather than having complete four modalities as in BraTS dataset, it is common to have missing modalities in clinical scenarios. We design a brain tumor segmentation algorithm that is robust to the absence of any modality. Our network includes a channel-independent encoding path and a feature-fusion decoding path. We use self-supervised training through channel dropout and also propose a novel domain adaptation method on feature maps to recover the information from the missing channel. Our results demonstrate that the quality of the segmentation depends on which modality is missing. Furthermore, we also discuss and visualize the contribution of each modality to the segmentation results. Their contributions are along well with the expert screening routine.",
keywords = "Brain tumor segmentation, Domain adaptation, Multi-modality, Self-supervised learning",
author = "Yan Shen and Mingchen Gao",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 26th International Conference on Information Processing in Medical Imaging, IPMI 2019 ; Conference date: 02-06-2019 Through 07-06-2019",
year = "2019",
doi = "10.1007/978-3-030-20351-1\_32",
language = "English",
isbn = "9783030203504",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "417--428",
editor = "Gee, \{James C.\} and Yushkevich, \{Paul A.\} and Chung, \{Albert C.S.\} and Siqi Bao",
booktitle = "Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings",
address = "Germany",
}