Improved YOLOX Framework for Automatic Large Intracranial Artery Stenosis Detection in Digital Subtraction Angiography Images

Ischemic stroke has a very high mortality and disability rate, and intracranial artery stenosis is an important cause of ischemic stroke. At present, transvascular interventional surgery is an effective remedy to treat intracranial artery stenosis, and as the gold standard in surgery, Digital Subtraction Angiography (DSA) images can effectively display the outline of blood vessels and the flow of blood. Detecting and locating the stenosis from DSA images is a challenging problem due to the large variation in the thickness of the blood vessel and the complex shape of the blood vessel. In this paper, we collect a dataset with 2860 DSA sequence samples and annotate stenosis locations, constructing the first automatic detection and localization method for stenosis in DSA images. In addition, considering that the commonly used Intersection-over-Union (IoU) loss ignores the similarity indicators of the image patches in the prediction box and the ground-truth (GT) box, a plug-and-play loss function that considers the image similarity between the prediction box and the GT box is proposed to effectively improve network performance. Extensive experiments demonstrate the effectiveness of our approach, which outperforms classical detectors.