Lumen Segmentation

Lumen segmentation, the automated identification of the inner boundaries of tubular structures in medical images, is crucial for accurate diagnosis and treatment planning across various medical specialties. Current research focuses on improving the accuracy and robustness of segmentation algorithms, particularly using deep learning architectures like U-Nets and their variations, often incorporating geometric constraints or multi-task learning to handle the complexities of different imaging modalities and anatomical structures. These advancements are driving improvements in procedures such as stent sizing in interventional cardiology, brain vessel analysis for stroke treatment, and minimally invasive lung procedures, ultimately leading to more precise and effective patient care.