Automatic Segmentation of Prostate Magnetic Resonance Imaging Using Generative Adversarial Networks

Abstract Background: Automatic and detailed segmentation of the prostate using magnetic resonance imaging (MRI) plays an essential role in prostate imaging diagnosis. However, the complexity of the prostate gland hampers accurate segmentation from other tissues. Thus, we propose the automatic prostate segmentation method SegDGAN, which is based on a classic generative adversarial network (GAN) model. Methods: The proposed method comprises a fully convolutional generation network of densely connected blocks and a critic network with multi-scale feature extraction. In these computations, the objective function is optimized using mean absolute error and the Dice coefficient, leading to improved accuracy of segmentation results and correspondence with the ground truth. The common and similar medical image segmentation networks U-Net, fully convolution network, and SegAN were selected for qualitative and quantitative comparisons with SegDGAN using a 220-patient dataset and the publicly available dataset PROMISE12. The commonly used segmentation evaluation metrics Dice similarity coefficient (DSC), volumetric overlap error (VOE), average surface distance (ASD), and Hausdorff distance (HD) were also used to compare the accuracy of segmentation between these methods. Results: SegDGAN achieved the highest DSC value of 91.66%, the lowest VOE value of 23.47% and the lowest ASD values of 0.46 mm with the clinical dataset. In addition, the highest DCS value of 88.69%, the lowest VOE value of 23.47%, the lowest ASD value of 0.83 mm, and the lowest HD value of 11.40 mm was achieved with the PROMISE12 dataset. Conclusions: Our experimental results show that the SegDGAN model outperforms other segmentation methods Keywords: Automatic segmentation, Generative adversarial networks, Magnetic resonance imaging, Prostate