论文标题
在合成图像上训练的多对比度MRI分割
Multi-Contrast MRI Segmentation Trained on Synthetic Images
论文作者
论文摘要
在我们的全面实验和评估中，我们表明可以生成多个对比度（甚至是合成的），并使用合成生成的图像来训练图像分割引擎。我们显示了在描绘肌肉，脂肪，骨骼和骨髓的实际多对比度MRI扫描测试的有希望的分割结果，这些结果均接受了合成图像的训练。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 \％，95.33 \％，分别用于肌肉，脂肪，骨头，骨骼和骨髓划分。结果与用于分割训练时获得的结果没有显着差异：94.68 \％，94.67 \％，95.91 \％和96.82 \％。
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.
