Multi-Contrast MRI Segmentation Trained on Synthetic Images
Conference paper > Full Text · 2022
Authors: İsmail IRMAKÇI, Zeki Emre Unel, Nazli Ikizler-Cinbis, Ulas Bagci
44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, United States of America, Glasgow, International, Invitee, Full Text, 2022-2022, vol 2022-July, pp 5030-5034
DOI: 10.1109/EMBC48229.2022.9871119
Summary
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. Clinical relevance - Synthetically generated images could potentially be used in large-scale training of deep networks for segmentation purpose. Small data set problem of many clinical imaging problems can potentially be addressed with the proposed algorithm.