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2020 | OriginalPaper | Buchkapitel

Realistic Adversarial Data Augmentation for MR Image Segmentation

verfasst von : Chen Chen, Chen Qin, Huaqi Qiu, Cheng Ouyang, Shuo Wang, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Verlag: Springer International Publishing

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Abstract

Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios.

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Vorheriges Kapitel Domain Adaptive Relational Reasoning for 3D Multi-organ Segmentation

Nächstes Kapitel Learning to Segment Anatomical Structures Accurately from One Exemplar

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https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html.

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Titel: Realistic Adversarial Data Augmentation for MR Image Segmentation
verfasst von: Chen Chen
Chen Qin
Huaqi Qiu
Cheng Ouyang
Shuo Wang
Liang Chen
Giacomo Tarroni
Wenjia Bai
Daniel Rueckert
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Print ISBN: 978-3-030-59709-2

Electronic ISBN: 978-3-030-59710-8

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-59710-8_65

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