Recent Advances in Medical Imaging Segmentation: A Survey
Medical image segmentation faces fundamental challenges including data scarcity, high annotation costs, poor cross-modal and cross-domain generalizability, and stringent privacy constraints. To address these, this work systematically reviews over 200 state-of-the-art publications and—uniquely—integrates perspectives from generative AI (e.g., diffusion models) and foundation models into a clinically oriented evaluation framework. We delineate adaptation pathways for foundation models in medical segmentation, identify critical bottlenecks (e.g., domain misalignment, computational overhead), and propose lightweight fine-tuning strategies—including visual prompting, multimodal fusion, and self-supervised pretraining. Our contributions include a continuously updated, open-source knowledge repository on GitHub, featuring a structured technical roadmap that bridges algorithmic innovation with clinical translation. This resource supports both methodological development and real-world deployment of segmentation models in healthcare settings.