CycleGAN Models for MRI Image Translation
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Abstract (arXiv)
Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class is limited. From the learning perspective, this process contributes to data-oriented robustness of the model by inherently broadening the model's exposure to more diverse visual data and enabling it to learn more generalized features. In the case of generating additional neuroimages, it is advantageous to obtain unidentifiable medical data and augment smaller annotated datasets. This study proposes the development of a CycleGAN model for translating neuroimages from one field strength to another (e.g., 3 Tesla to 1.5). This model was compared to a model based on DCGAN architecture. CycleGAN was able to generate the synthetic and reconstructed images with reasonable accuracy. The mapping function from the source (3 Tesla) to target domain (1.5 Tesla) performed optimally with an average PSNR value of 25.69 ± 2.49 dB and an MAE value of 2106.27 ± 1218.37.
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References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
- 2017Deep MR to CT Synthesis using Unpaired Datacited by 86
- 2018GANs for Medical Image Analysiscited by 81
- 2018Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data sizecited by 42
- 2020Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domaincited by 24
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