The Evolution of Network Structures for Intelligent Segmentation of Medical Images Based on U-Net
With the increasing demands in healthcare,deep learning technologies have shown tremendous potential in the field of automatic medical image segmentation.The advancement in spatial data intelligence provides novel solutions for achieving high-precision segmentation in medical imaging.As the most influential/prominent network architecture,U-Net has been widely applied across various medical imaging tasks since its introduction in 2015.Its distinctive encoder-decoder structure design not only establishes a fundamental paradigm for subsequent research but also has inspired numerous network improvements.A systematic review of key advancements in U-Net architecture development is provided.ResUNet addresses the training challenges in deep networks by incorporating residual connections,which enhances model stability and convergence.Attention-UNet improves feature selection accuracy within the skip connections by introducing adaptive attention mechanisms.TransUNet and Swin-UNet represent two crucial stages in incorporating modern Transformer architectures into medical image segmentation,demonstrating the significant potential of CNN-Transformer fusion for improving feature representation and segmentation accuracy.The evolutionary trend of medical image seg-mentation technology is highlighted from conventional CNN-based architectures to CNN-Transformer hybrid architectures.Additionally,the challenges faced by existing technologies are addressed and insights into future development/research directions are provided,provi-ding a comprehensive reference for further research in this field.
deep learningU-Netmedical image segmentationneural network architecture
万方数据
维普
