Anatomical Tree

Anatomical trees, representing branching structures like blood vessels or airways, are crucial for medical diagnosis and treatment planning. Current research focuses on accurately representing these complex structures using novel methods such as implicit neural representations and hierarchical segmentation models, often incorporating deep learning architectures like UNets and Transformers to improve efficiency and accuracy. These advancements aim to improve automated analysis of medical images, enabling faster and more precise identification of anatomical features for applications ranging from interventional radiology to pathology. The ultimate goal is to leverage these improved representations for better clinical decision-making.