Realistic Adversarial Data Augmentation for MR Image Segmentation

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020
Subtitle of host publication23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I
ISBN (Electronic)78-3-030-59710-8
ISBN (Print)978-3-030-59709-2
Publication statusPublished - 29 Sept 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Link
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020


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