Cardiac Magnetic Resonance Image Segmentation Based on GAN and Axial Block Attention
Cardiac magnetic resonance image segmentation is of great significance for cardiac function analysis and cardiac disease diagnosis.Aiming at the problem that traditional cardiac segmentation methods can not fully extract features from cardiac MR images,and the Deep Learning method based on the Attention Mechanism has too many parameters.A cardiac magnetic resonance image segmentation model based on GAN and axial block attention is designed to extract image features at multiple scales and in all aspects,and combined with GAN strategy to improve model performance.Experimental results show that the model achieves effective segmentation of images and improves the consistency between segmentation results and real labels.
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