VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs

Radiographs are used as the most critical imaging tool for identifying spine anomalies in clinical practice [1]. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. To the best of our knowledge, no existing studies are devoted to developing and evaluating a comprehensive system for classifying and localizing multiple spine lesions from X-ray scans. The lack of large-scale spine X-ray datasets with high-quality images and human expert annotations is the key obstacle. To fill this gap, we introduce a large-scale annotated medical image dataset for spinal lesion detection and classification from radiographs. The dataset, called VinDr-SpineXR, contains 10,466 spine X-ray images from 5,000 studies, each of which is manually annotated with 13 types of abnormalities by an experienced radiologist with bounding boxes around abnormal findings. This is the largest dataset to date that provides radiologist's bounding-box annotations for developing supervised-learning algorithms for spine X-ray analysis.