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，放射影像片（radiographs）是识别脊柱异常的核心影像学检查工具[1]。然而，放射科医师在评估脊柱骨病变时仍面临极大挑战。据我们所知，目前尚无研究致力于开发并评估一套可从X线扫描影像中对多种脊柱病变完成分类与定位的综合性系统。当前面临的核心阻碍之一，便是缺乏兼具高质量影像与专业人工标注的大规模脊柱X线数据集。为填补这一研究空白，我们构建了一款用于从放射影像片中检测并分类脊柱病变的大规模标注医学影像数据集。该数据集命名为VinDr-SpineXR，包含来自5000项研究的10466张脊柱X线影像，每张影像均由经验丰富的放射科医师针对13类异常病变完成手动标注，并为每一处异常发现绘制边界框（bounding boxes）。这是目前规模最大的、可用于开发脊柱X线分析监督学习算法的、带有放射科医师标注边界框的数据集。

搜集汇总

背景与挑战

背景概述

VinDr-SpineXR是一个大规模标注的脊柱X光影像数据集，包含10,466张图像和13种脊柱病变类型的手动标注，旨在支持脊柱病变的检测和分类算法开发。数据集分为训练集和测试集，分别用于模型训练和评估。