Segmentation Framework

Segmentation frameworks are computational tools designed to partition images or volumes into meaningful regions, a crucial step in various applications. Current research emphasizes improving accuracy and efficiency across diverse data types, including medical images, remote sensing imagery, and LiDAR scans, utilizing architectures like Transformers, CNNs, and state-space models, often incorporating attention mechanisms and multi-scale feature extraction. These advancements are driving progress in fields such as medical diagnosis, autonomous driving, and environmental monitoring by enabling automated analysis of complex visual data. The development of weakly-supervised and even unsupervised methods is also a significant focus, aiming to reduce the reliance on extensive manual annotation.