IRAU-Net: Inception Residual Attention U-Net in Adversarial Network for Cardiac MRI Segmentation

Due to the significant advancements in medical imaging, the application of artificial intelligence for early disease diagnosis has greatly contributed to reducing mortality caused by heart diseases. Cardiac MRI segmentation into the left ventricle (LV), right ventricle (RV) and myocardium (MYO) is a key step towards the early treatment and diagnosis of heart diseases. The proposed method consists of two main components: a localization part to extract the heart organ from other parts of the image, and a GAN model for segmentation. In the generator part of the generative adversarial network (GAN) model, a customized U-Net network is employed to segment the cardiac image. A combination of inception and residual blocks and utilizing an attention mechanism yield an improved version of U-Net. On the other hand, the discriminator part of the GAN model is designed to accurately distinguish between the ground truth image and the segmented image generated by the generator part. Our method is evaluated on two cardiac MRI datasets. The evaluation results on the ACDC 2017 challenge dataset show mean Dice scores of 0.947 for LV, 0.919 for RV, and 0.907 for MYO in cardiac MRI segmentation. The experimental results highlight that our proposed IRAU-Net method outperforms other state-of-the-art methods in terms of accuracy while significantly reducing computational costs.