Researchers are exploring various deep learning-based architectures for image segmentation tasks, including urban change detection and medical applications such as brain tumor and polyp segmentation. Different models are being proposed, including dual-stream hybrid architectures and those incorporating boundary-aware mechanisms, vision transformers, and graph convolutional networks. The development of these models aims to improve the accuracy of image segmentation in various fields, but the most effective approach is not yet clear.