Artery Segmentation

Artery segmentation, specifically focusing on intracranial arteries in Digital Subtraction Angiography (DSA) sequences, aims to automatically delineate blood vessels from medical images to aid in diagnosing cerebrovascular diseases. Current research emphasizes using deep learning models, often incorporating spatio-temporal architectures like recurrent neural networks (RNNs) or transformers, to leverage information across multiple DSA frames and improve segmentation accuracy, particularly for small vessels. The development of publicly available datasets and standardized benchmarks is crucial for advancing this field, which has significant implications for improving the diagnosis and treatment of stroke and other cerebrovascular conditions.