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28 March 2023 Study for CT to MRI translation based on cycle-GAN and UNet-GAN
Yuhui Lai
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Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256632 (2023) https://doi.org/10.1117/12.2668052
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China
Abstract
MRI and CT are both important medical imaging modalities, but MRI and CT imaging are done in different ways, each with its own advantages and disadvantages. Obtaining both images at the same time can help physicians make better decisions about treatment options. However, due to various limitations, some patients can only obtain one type of image. Therefore, it is necessary to find a well-performing GAN to transform MRI and CT images. In this paper, the effect of Cycle-GAN with different activation functions is compared, such as LeakyRELU, and different number of layers in MRI-CT conversion. Also, this article compares the effects of Cycle-GAN and UNet-GAN. The results indicate that the Cycle-GAN model using LeakyRELU as the activation function is better than the Cycle-GAN model using RELU as the activation function. Second, the effect of deepening the layers of the GAN model is worse than that of the base model. And the effect of UNet-GAN is similar to that of Cycle-GAN. This is not quite as expected, because Cycle-GAN has one more discriminator than UNet-GAN, and the effect should be better. But the experimental results do not confirm this conclusion.
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Yuhui Lai "Study for CT to MRI translation based on cycle-GAN and UNet-GAN", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256632 (28 March 2023); https://doi.org/10.1117/12.2668052
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KEYWORDS
Data modeling
Computer programming
Education and training
Machine learning
Neural networks
Medical imaging
Performance modeling
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