Interactive Segmentation Model

Interactive segmentation models leverage user input, such as points, boxes, or scribbles, to guide the segmentation of images or volumes, aiming to improve accuracy and efficiency compared to fully automated or manual methods. Current research focuses on improving model robustness, speed, and the handling of diverse prompt types, often employing transformer-based architectures and incorporating techniques like iterative refinement and confidence learning. These advancements are particularly impactful in medical imaging, where they can accelerate annotation of complex datasets like 3D ultrasound scans and whole-body PET images, ultimately improving diagnostic accuracy and workflow efficiency.