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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.
Original language | English |
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Title of host publication | Simulation and Synthesis in Medical Imaging |
Subtitle of host publication | Second International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10, 2017, Proceedings |
Editors | Sotirios Tsaftaris, Ali Gooya, Alejandro Frangi, Jerry Prince |
Publisher | Springer International Publishing |
Pages | 3-13 |
Number of pages | 11 |
Volume | 10557 |
Edition | 1 |
ISBN (Electronic) | 978-3-319-68127-6 |
ISBN (Print) | 9783319681269 |
DOIs | |
Publication status | Published - 26 Sep 2017 |
Event | 2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 10 Sep 2017 → 10 Sep 2017 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10557 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 2nd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2017 Held in Conjunction with the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 |
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Country/Territory | Canada |
City | Quebec City |
Period | 10/09/17 → 10/09/17 |
Keywords
- Cardiac
- CT
- Deep learning
- GAN
- MR
- Synthesis
Fingerprint
Dive into the research topics of 'Adversarial Image Synthesis for Unpaired Multi-Modal Cardiac Data'. Together they form a unique fingerprint.Projects
- 2 Finished
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TRANSFER: RELIABLE EVALUATION OF CORONARY ARTERY DISEASE USING MYOCARDIAL BOLD MRI WITH CO2
1/06/16 → 31/05/17
Project: Research