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.

放射学影像在临床实践中被公认为辨识脊椎畸变的最关键成像手段[1]。然而，对脊椎骨病变的评估对放射科医生而言是一项极具挑战性的任务。据我们所知，截至目前，尚无现有研究致力于开发与评估一套全面系统，用于从X光扫描中分类与定位多发性脊椎病变。缺乏大规模、高质量图像及人类专家标注的脊椎X光数据集是这一领域的主要障碍。为了填补这一空白，我们引入了一个大规模标注医疗影像数据集，旨在进行脊椎病变的检测与分类，该数据集命名为VinDr-SpineXR，包含来自5,000个病例的10,466张脊椎X光影像，每张影像均由经验丰富的放射科医生通过绘制异常发现处的边界框进行13种异常类型的手动标注。这是迄今为止提供放射科医生边界框标注的最大数据集，可用于开发脊椎X光分析的监督学习算法。