This article provides a comprehensive review of deep learning techniques for medical image segmentation, highlighting their achievements and challenges. It begins by introducing the importance of medical image segmentation in diagnostic and treatment processes, emphasizing the shift from traditional methods to deep learning approaches. The authors discuss various network structures, including Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, and Recurrent Neural Networks (RNNs), detailing their architectures, advantages, and limitations. They also explore training techniques such as deeply supervised learning, weakly supervised learning, and transfer learning, and address common challenges like limited annotated data, class imbalance, overfitting, and gradient vanishing. The article concludes by discussing solutions to these challenges and emphasizing the potential of deep learning in advancing medical image segmentation.This article provides a comprehensive review of deep learning techniques for medical image segmentation, highlighting their achievements and challenges. It begins by introducing the importance of medical image segmentation in diagnostic and treatment processes, emphasizing the shift from traditional methods to deep learning approaches. The authors discuss various network structures, including Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, and Recurrent Neural Networks (RNNs), detailing their architectures, advantages, and limitations. They also explore training techniques such as deeply supervised learning, weakly supervised learning, and transfer learning, and address common challenges like limited annotated data, class imbalance, overfitting, and gradient vanishing. The article concludes by discussing solutions to these challenges and emphasizing the potential of deep learning in advancing medical image segmentation.