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.

在临床实践中，X线平片（radiographs）是识别脊柱异常最为关键的影像学检查手段[1]。然而，脊柱骨病变的评估对于放射科医师而言仍是一项极具挑战性的工作。据我们所知，目前尚无研究致力于开发并评估一套可从X线扫描图像中对多种脊柱病变进行分类与定位的完整系统。当前面临的核心阻碍，正是缺乏具备高质量图像与人工专家标注的大规模脊柱X线数据集。为填补这一研究空白，我们构建了一款用于X线平片脊柱病变检测与分类的大规模标注医学影像数据集。该数据集名为VinDr-SpineXR，包含来自5000例检查的10466张脊柱X线图像，每一张图像均由一名经验丰富的放射科医师手动标注了13类异常病变，并为每一处异常绘制了边界框（bounding boxes）。这是目前规模最大的、配备放射科医师标注边界框的数据集，可用于开发脊柱X线分析相关的监督学习算法。

搜集汇总

背景与挑战

背景概述

VinDr-SpineXR是一个包含10,466张脊柱X光图像的大规模医学影像数据集，每张图像都标注了13种脊柱病变类型和位置，用于开发和评估脊柱病变检测与分类算法。