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.
翻译：在我们的全面实验和评估中,我们证明有可能产生多重对比(甚至全部合成的),并利用合成生成的图像来训练一个图像分割引擎。我们展示了在对肌肉、脂肪、骨头和骨髓进行分解时,通过真实的多盘式磁共振扫描测试的有希望的分化结果,这些扫描都是经过合成图像培训的。根据合成图像培训,我们的分解结果分别高达93.91 ⁇ 、94.11 ⁇ 、91.63 ⁇ 、95.33 ⁇,肌肉、脂肪、骨骼和骨髓的分解。结果与在分解培训中使用真实图像时获得的结果没有多大区别:94.68 ⁇ 、94.67 ⁇ 、95.91 ⁇ 和96.82 ⁇ 。
相关内容
微信扫码咨询专知VIP会员