Centerline Detection

Centerline detection aims to automatically identify the central axis of tubular structures in images, crucial for various applications from medical image analysis (e.g., brain vessels, coronary arteries, ribs) to autonomous driving (road lane detection). Recent research emphasizes the use of deep learning models, particularly transformer architectures and UNets, often incorporating instance segmentation and topology-aware loss functions to improve accuracy and handle complex structures. These advancements enable more precise and efficient analysis in diverse fields, improving diagnostic capabilities in medicine and enhancing the safety and reliability of autonomous vehicles.