Anatomical Landmark Detection

Anatomical landmark detection focuses on automatically identifying key anatomical points in medical images, aiding in diagnosis, treatment planning, and quantitative analysis. Current research emphasizes improving accuracy and efficiency through various deep learning architectures, including U-Nets, transformers, and diffusion models, often employing heatmap regression or other generative modeling techniques to represent landmark probability. These advancements aim to reduce the time and expertise required for manual landmark identification, ultimately improving the speed and consistency of medical image analysis and potentially leading to better patient care. Furthermore, research is actively addressing challenges such as handling domain shifts, multi-modal image analysis, and quantifying prediction uncertainty.