Deep Learning for Semantic Segmentation of Natural and Medical Images

This review provides a comprehensive overview of deep learning-based approaches for semantic segmentation of natural and medical images, categorizing the literature into six main groups: architectural improvements, optimization function-based improvements, data synthesis-based improvements, sequenced models, weakly supervised methods, and multi-task models. The review analyzes the contributions and limitations of each group and presents potential future research directions.