Multi-Contrast MRI Segmentation Trained on Synthetic Images

Ismail Irmakci; Zeki Emre Unel; Nazli Ikizler-Cinbis; Ulas Bagci

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arXiv · 2207.02469

Multi-Contrast MRI Segmentation Trained on Synthetic Images

Ismail Irmakci·Zeki Emre Unel·Nazli Ikizler-Cinbis·Ulas Bagci

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Abstract

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.

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BibTeX RIS

Ismail Irmakci, Zeki Emre Unel, Nazli Ikizler-Cinbis, Ulas Bagci. 2022-07-06. Multi-Contrast MRI Segmentation Trained on Synthetic Images. https://arxiv.org/abs/2207.02469

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