作者采用cycle-GAN的思想进行MR和CT图像的模态转换。利用循环一致性实现在非对称数据下图像的模态转换。
损失
方法
backward cycle
评价指标
1）mean absolute error
2）peak-signal-to-noise-ratio (PSNR)
实验结果
Fig. 4: From left to right Input MR image, synthesized CT image, reference real
CT image, and absolute error between real and synthesized CT image.
Fig. 5: From left to right Input MR image, synthesized CT image with paired
training, synthesized CT image with unpaired training, reference real CT image.
Fig. 6: From left to right Input MR image, synthesized CT image, reconstructed
MR image, and relative error between the input and reconstructed MR image
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