Optimizing Network Architectures for Ultrasound Cardiac Segmentation

Cardiac image segmentation is critical for medical diagnosis and treatment planning. Traditional approaches often face accuracy challenges. In this study, we propose a deep learning-based method that incorporates architectural improvements and optimization techniques to overcome these limitations. Our method integrates skip connections, a spatial attention mechanism, and label smoothing for enhanced segmentation performance. Experimental results on the CAMUS dataset show that our approach surpasses baseline models, achieving superior segmentation accuracy. Specifically, our method increases the mean Intersection over Union (mIoU) from 0.8141 (U-Net) to 0.8428 (Residual Attention U-Net) and the mean Dice score from 0.8948 (U-Net) to 0.9127 (Residual Attention U-Net). The proposed method has potential applications in medical diagnosis, disease prevention, and treatment planning, emphasizing its practical significance in cardiac image segmentation.