Research on obtaining pseudo CT images based on stacked generative adversarial network

Abstract

Background: To investigate the feasibility of using a stacked generative adversarial network (sGAN) to synthesize pseudo computed tomography (CT) images based on ultrasound (US) images. Methods:The pre-radiotherapy US and CT images of 75 patients with cervical cancer were selected for the training set of pseudo-image synthesis. In the first stage, labeled US images were used as the first conditional GAN input to obtain low-resolution pseudo CT images, and in the second stage, a superresolution reconstruction GAN was used. The pseudo CT image obtained in the first stage was used as an input, following which a high-resolution pseudo CT image with clear texture and accurate grayscale information was obtained. Five cross validation tests were performed to verify our model. The mean absolute error (MAE) was used to compare each pseudo CT with the same patient's real CT image. Also, another 10 cases of patients with cervical cancer, before radiotherapy, were selected for testing, and the pseudo CT image obtained using the neural style transfer (NSF) and CycleGAN methods were compared with that obtained using the sGAN method proposed in this study. Finally, the dosimetric accuracy of pseudo CT images was verified by phantom experiments. Results:The MAE metric values between the pseudo CT obtained based on sGAN, and the real CT in five-fold cross validation are 66.82±1.59 HU, 66.36±1.85 HU, 67.26±2.37 HU, 66.34±1.75 HU, and 67.22±1.30 HU, respectively. The results of the metrics, namely, normalized mutual information (NMI), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR), between the pseudo CT images obtained using the sGAN method and the ground truth CT (CTgt) images were compared with those of the other two methods via the paired t-test, and the differences were statistically significant. The dice similarity coefficient (DSC) measurement results showed that the pseudo CT images obtained using the sGAN method were more similar to the CTgt images of organs at risk. The dosimetric phantom experiments also showed that the dose distribution between the pseudo CT images synthesized by the new method was similar to that of the CTgt images.Conclusions: Compared with NSF and CycleGAN methods, the sGAN method can obtain more accurate pseudo CT images, thereby providing a new method for image guidance in radiotherapy for cervical cancer.

HLSNC-GAN: Medical Image Synthesis Using Hinge Loss and Switchable Normalization in CycleGAN

“…They introduced batch normalization in the model's discriminator to prevent overfitting. Sun et al [35] proposed a Stacked GAN (sGAN) model based on residual network (ResNet) and FCN. They used batch normalization (BN) layers in the generator (G) and discriminator (D) to speed up the training of the network and convergence.…”