Segmentation of Medical Image Based on U-Net Neural Network

Medical image segmentation is a critical task in healthcare diagnostics and treatment planning. Among various approaches, the U-Net neural network has emerged as a revolutionary and efficient method, particularly well-suited for medical imaging applications. Its unique architecture facilitates robust feature extraction and demonstrates superior adaptability to limited datasets, which often present challenges such as indistinct anatomical boundaries. This study proposes an enhanced U-Net model that incorporates a residual module and a comprehensive skip connection mechanism. This innovative design enables the decoder to integrate multi-scale feature maps: fine-grained information from the encoder, equivalent-scale features, and broader contextual data from the decoder itself. Experimental results demonstrate that this modified U-Net architecture achieves higher segmentation accuracy and improved training efficiency compared to conventional methods.