State-of-the-art analysis of deep learning techniques for image segmentation

Image segmentation is an important computer vision problem that captures a particular region of interest by leveraging per-pixel image classification. It has significant applications in various fields, such as biomedical engineering, remote sensing, autonomous driving, etc. There has been a gradual evolution towards deep learning image segmentation methods, including convolutional neural networks, recurrent neural networks, encoder-decoder architectures, and generative adversarial networks. This chapter provides a description of deep learning image segmentation networks, offering insights into their architectural details, performance assessment metrics, advantages, and disadvantages.