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2017 | OriginalPaper | Buchkapitel

Adversarial Training and Dilated Convolutions for Brain MRI Segmentation

verfasst von : Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim

Erschienen in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Verlag: Springer International Publishing

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Abstract

Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to distinguish from real images.

In this study we use an adversarial training approach to improve CNN-based brain MRI segmentation. To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations. During training, this loss function is optimised together with the conventional average per-voxel cross entropy loss.

The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.

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Titel: Adversarial Training and Dilated Convolutions for Brain MRI Segmentation
verfasst von: Pim Moeskops
Mitko Veta
Maxime W. Lafarge
Koen A. J. Eppenhof
Josien P. W. Pluim
Verlag: Springer International Publishing
Buch: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
Print ISBN: 978-3-319-67557-2

Electronic ISBN: 978-3-319-67558-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-67558-9_7

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