Automatic Segmentation

Automatic segmentation aims to computationally identify and delineate specific regions of interest within images, streamlining analysis across diverse fields. Current research heavily utilizes deep learning models, particularly U-Net and its variants, along with transformer-based architectures like Swin UNETR and the Segment Anything Model (SAM), often incorporating techniques like semi-supervised learning and prompt engineering to improve accuracy and efficiency. This technology significantly impacts various domains, from medical image analysis (e.g., organ segmentation for radiotherapy planning) and remote sensing (e.g., infrastructure mapping) to materials science (e.g., analyzing additive manufacturing processes), accelerating research and improving diagnostic capabilities.