Key Technologies Analysis of Deep Learning-based Medical Image Segmentation

This study explores Deep Learning (DL) methods in medical image segmentation. By precisely delineating anatomical structures and focal areas, it enhances the precision of diagnostic and treatment strategies. With an emphasis on the architecture design, performance advantages, and clinical application scenarios of Convolutional Neural Network (CNN), Fully Convolutional Network (FCN), and U-shaped Convolutional Network (U-Net) models, the research work examined the fundamentals of DL-based medical image segmentation technology. The working mechanism of these models, their adaptability to multimodal images, and their practical value in clinical scenarios. DL models perform better in precision as well as effectiveness when compared to traditional methods. Specifically, the hierarchical feature extraction capability of CNN shows robustness in feature extraction for organ segmentation, the pixel-level prediction mechanism of FCN is outstanding in fine-grained segmentation tasks, and the encoder-decoder structure of U-Net, combined with skip connections, improves segmentation accuracy. Compared to traditional methods, DL models demonstrate superior performance in terms of accuracy and efficiency. Specifically, CNN's hierarchical feature extraction capability exhibits robustness in feature extraction for organ segmentation. The pixel-level prediction mechanism of FCN stands out in fine-grained segmentation tasks. Additionally, the encoder-decoder architecture of U-Net, combined with skip connections, improves segmentation accuracy, although this requires significant computational resources.