Liver Segmentation Based on Improved U-Net Network

Contemporary medical image segmentation techniques play a crucial role in the field of medical image processing, with liver segmentation being a key task critical for clinical diagnosis and treatment planning. However, due to the complex anatomy of the liver and the diversity of image quality, traditional segmentation methods often struggle to meet practical needs. To address this issue, this study adopts deep learning techniques and proposes three different architectures of deep neural network models, including UNet, NestedUNet, and ResUNet, for liver segmentation tasks. Through training and optimization on medical image datasets, these models employ Dice loss functions and data augmentation techniques to improve accuracy and generalization of the models. Experimental results on multiple liver image datasets show that the proposed model achieves good performance in liver segmentation tasks. Using Dice coefficient, Jaccard coefficient, precision and recall, this paper verifies the effectiveness and robustness of the model on different datasets. These results provide an important reference for the further development of the field of medical image processing, and provide a reliable solution for the application of clinical medicine and medical imaging.