Achieving High Efficiency Heart Image Segmentation in U-Net by Means of Early Fusion and Contextual Information Reconstruction

Accurate segmentation of the ventricles provides quantitative data on the cardiac structure and function, facilitating precise diagnosis for medical professionals. Deep learning methods have been widely applied and proven highly effective in cardiac medical image segmentation however, challenges still remain. Due to the complexity of anatomical structures and the issues related to fine detail recovery in high - quality cardiac medical images, many current studies attempt to improve feature extraction and segmentation accuracy by increasing the network depth. While increasing network depth by introducing more convolutional layers and nonlinear transformations can effectively address issues such as complex morphology, high - quality cardiac images may still encounter problems such as overfitting, insufficient detail recovery, noise sensitivity, computational resource bottlenecks, and class imbalance, especially in deeper layers of the network. To address these challenges, this paper introduces EAS - Net. EAS - Net employs a classic U - shaped encoder architecture and incorporates a novel Residual Structure Unit RSU technique. Furthermore, the information prior to sampling, along with the process sampling information, is fused in advance and then input into the innovative Contextual Information Reconstruction CIR method and the Multi - Head Dilated Attention MHDA algorithm. This block effectively captures multi - scale contextual information, expands the receptive field, and significantly reduces computational complexity. Extensive experiments on multiple medical datasets demonstrate that EAS - Net exhibits high efficiency and robustness in high - quality cardiac image segmentation, particularly in left and right ventricular segmentation. It achieves exceptional performance while maintaining a low model complexity.