461-01-016 ISMRM Abstract

Supervised DE-MRI Infarct Segmentation with Novel Synthetic Data Generation via Simulation and Conditional GAN

Primary: Cardiovascular - Myocardium

Secondary: Analysis Methods - Segmentation and Detection

461-01-016 · Cardiac Tissue Characterization · Tuesday, 12 May, 8:20 AM–9:15 AM · Digital Posters Row B

Keywords: Image synthesis Myocardial infarction Deep Learning Segmentation Generative Adversarial Networks Delayed Myocardial Enhancement

Accepted

Omar Abdelbar¹, Hana Hesham¹, Omar Othman^1,2, Khaled Badr¹, Mishkat Habib³, Tamer Y Basha¹, Ahmed Gharib³, Ahmed Abdelfadeel³, Khaled Z Abd-Elmoniem ³

¹Systems and Biomedical Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt

²Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany

³Biomedical and Metabolic Imaging Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, United States of America

Presenting Author: Khaled Z Abd-Elmoniem

Synopsis

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References

1. Lalande A, Chen Z, Decourselle T, et al. EMIDEC: A Database Usable for the Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI. Data.2020;5(4):89. https://doi.org/10.3390/data5040089 [doi]

2. Lalande A, Chen Z, Pommier T, et al. Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge. Med Image Anal.2022;79:102428. https://doi.org/10.1016/j.media.2022.102428 [doi]

3. Wu E, Ortiz JT, Tejedor P, et al. Infarct size by contrast enhanced cardiac magnetic resonance is a stronger predictor of outcomes than left ventricular ejection fraction or end-systolic volume index. Heart. 2008;94(6):730-6. https://doi.org/10.1136/hrt.2007.122622 [doi]

4. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods.2021;18(2):203-211. https://doi.org/10.1038/s41592-020-01008-z [doi]

5. Isola P, Zhu JY, Zhou T, Efros AA. Image-to-Image Translation with Conditional Adversarial Networks. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR);2017. https://doi.org/10.1109/CVPR.2017.632 [doi]

6. Zhu JY, Park T, Isola P, Efros AA. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In: Proc IEEE Int Conf Comput Vis (ICCV); 2017. https://doi.org/10.1109/ICCV.2017.244 [doi]

7. Oktay O, Schlemper J, Folgoc LL, et al. Attention U-Net: Learning Where to Look for the Pancreas. arXiv preprint arXiv:1804.03999. 2018. https://arxiv.org/abs/1804.03999

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http://echo.ismrm.org/p/ISMRM2026/461-01-016