Multi-Contrast MRI Segmentation Trained on Synthetic Images
| Autor: | Ismail Irmakci, Zeki Emre Unel, Nazli Ikizler-Cinbis, Ulas Bagci |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition FOS: Electrical engineering electronic engineering information engineering Records Electrical Engineering and Systems Science - Image and Video Processing Magnetic Resonance Imaging Algorithms Article Machine Learning (cs.LG) |
| Zdroj: | Annu Int Conf IEEE Eng Med Biol Soc |
| DOI: | 10.1109/embc48229.2022.9871119 |
| Popis: | In our comprehensive experiments and evaluations, we show that it is possible to generate multiple contrast (even all synthetically) and use synthetically generated images to train an image segmentation engine. We showed promising segmentation results tested on real multi-contrast MRI scans when delineating muscle, fat, bone and bone marrow, all trained on synthetic images. Based on synthetic image training, our segmentation results were as high as 93.91\%, 94.11\%, 91.63\%, 95.33\%, for muscle, fat, bone, and bone marrow delineation, respectively. Results were not significantly different from the ones obtained when real images were used for segmentation training: 94.68\%, 94.67\%, 95.91\%, and 96.82\%, respectively. Comment: IEEE EMBC 2022 conference (oral) paper |
| Databáze: | OpenAIRE |
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