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2017 | OriginalPaper | Buchkapitel

Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data

verfasst von : Agisilaos Chartsias, Thomas Joyce, Rohan Dharmakumar, Sotirios A. Tsaftaris

Erschienen in: Simulation and Synthesis in Medical Imaging

Verlag: Springer International Publishing

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Abstract

This paper demonstrates the potential for synthesis of medical images in one modality (e.g. MR) from images in another (e.g. CT) using a CycleGAN [24] architecture. The synthesis can be learned from unpaired images, and applied directly to expand the quantity of available training data for a given task. We demonstrate the application of this approach in synthesising cardiac MR images from CT images, using a dataset of MR and CT images coming from different patients. Since there can be no direct evaluation of the synthetic images, as no ground truth images exist, we demonstrate their utility by leveraging our synthetic data to achieve improved results in segmentation. Specifically, we show that training on both real and synthetic data increases accuracy by 15% compared to real data. Additionally, our synthetic data is of sufficient quality to be used alone to train a segmentation neural network, that achieves 95% of the accuracy of the same model trained on real data.

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Nächstes Kapitel Deep MR to CT Synthesis Using Unpaired Data

https://keras.io.

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Titel: Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data
verfasst von: Agisilaos Chartsias
Thomas Joyce
Rohan Dharmakumar
Sotirios A. Tsaftaris
Verlag: Springer International Publishing
Buch: Simulation and Synthesis in Medical Imaging
Print ISBN: 978-3-319-68126-9

Electronic ISBN: 978-3-319-68127-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-68127-6_1

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