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.