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Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis
- Source :
- ISBI
- Publication Year :
- 2020
- Publisher :
- arXiv, 2020.
-
Abstract
- Magnetic Resonance (MR) images of different modalities can provide complementary information for clinical diagnosis, but whole modalities are often costly to access. Most existing methods only focus on synthesizing missing images between two modalities, which limits their robustness and efficiency when multiple modalities are missing. To address this problem, we propose a multi-modality generative adversarial network (MGAN) to synthesize three high-quality MR modalities (FLAIR, T1 and T1ce) from one MR modality T2 simultaneously. The experimental results show that the quality of the synthesized images by our proposed methods is better than the one synthesized by the baseline model, pix2pix. Besides, for MR brain image synthesis, it is important to preserve the critical tumor information in the generated modalities, so we further introduce a multi-modality tumor consistency loss to MGAN, called TC-MGAN. We use the synthesized modalities by TC-MGAN to boost the tumor segmentation accuracy, and the results demonstrate its effectiveness.<br />Comment: 5 pages, 3 figures, accepted to IEEE ISBI 2020
- Subjects :
- FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
010501 environmental sciences
Fluid-attenuated inversion recovery
01 natural sciences
Multi modality
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Robustness (computer science)
medicine
FOS: Electrical engineering, electronic engineering, information engineering
0105 earth and related environmental sciences
medicine.diagnostic_test
business.industry
Image and Video Processing (eess.IV)
Pattern recognition
Magnetic resonance imaging
Electrical Engineering and Systems Science - Image and Video Processing
Image synthesis
Artificial intelligence
Mr images
business
Generative grammar
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- ISBI
- Accession number :
- edsair.doi.dedup.....20ef12135994d8f943f200bcbb59ca14
- Full Text :
- https://doi.org/10.48550/arxiv.2005.00925