Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:5030-5034. doi: 10.1109/EMBC48229.2022.9871119.
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.
在我们的全面实验和评估中，我们展示了生成多种对比度（甚至全部合成）并使用合成生成的图像来训练图像分割引擎是可行的。当在真实的多对比度 MRI 扫描上对肌肉、脂肪、骨骼和骨髓进行分割测试时，我们展示了有前景的分割结果，所有这些都是在合成图像上进行训练的。基于合成图像训练，我们的分割结果分别高达 93.91%、94.11%、91.63%和 95.33%，用于肌肉、脂肪、骨骼和骨髓的分割。使用真实图像进行分割训练获得的结果与之没有显著差异：分别为 94.68%、94.67%、95.91%和 96.82%。临床相关性-合成生成的图像可能可用于分割目的的深度网络的大规模训练。该算法可能可解决许多临床成像问题的数据量小的问题。
