2D Echocardiography Image Segmentation via Patch-Based Generative Adversarial Network

Accurate left ventricle (LV ) segmentation remains challenging due to the complex anatomical variability and imaging artifacts present in echocardiograms. Our ultimate objective is to develop a robust segmentation method for accurate segmentation of the LV , thereby enhancing the estimation of clinical indices. In this study, we introduce a novel hybrid generative adversarial network (GAN) architecture called patch-based GAN for cardiac image segmentation. Our contribution involves using two backbone models: UNET and a patch-based neural network (PBNN). UNET is employed for precise feature extraction and spatial preservation, aiding in constructing the latent space for the generator (G). Meanwhile, PBNN enhances the discriminator's ability to accurately distinguish between the generated mask and the ground truth mask. The proposed patch-based GAN framework allows the network to learn from both the input data and adversarial feedback, improving its ability to handle variations and artif...