Radiographs are used as the most important imaging tool for identifying spine anomalies in clinical practice. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. This work aims at developing and evaluating a deep learning-based framework, named VinDr-SpineXR, for the classification and localization of abnormalities from spine X-rays. First, we build a large dataset, comprising 10,468 spine X-ray images from 5,000 studies, each of which is manually annotated by an experienced radiologist with bounding boxes around abnormal findings in 13 categories. Using this dataset, we then train a deep learning classifier to determine whether a spine scan is abnormal and a detector to localize 7 crucial findings amongst the total 13. The VinDr-SpineXR is evaluated on a test set of 2,078 images from 1,000 studies, which is kept separate from the training set. It demonstrates an area under the receiver operating characteristic curve (AUROC) of 88.61% (95% CI 87.19%, 90.02%) for the image-level classification task and a mean average precision (mAP@0.5) of 33.56% for the lesion-level localization task. These results serve as a proof of concept and set a baseline for future research in this direction. To encourage advances, the dataset, codes, and trained deep learning models are made publicly available.
翻译:首先,我们建立了一个大型数据集,由5 000项研究的10,468个脊柱X射线图像组成,每个图像由拥有13类异常调查结果框的有经验的放射科医生手工附加说明。然而,对脊椎损伤的评估是放射科专家的一项艰巨任务。这项工作旨在开发和评价一个深层次学习框架,称为VinDr-SpineXR,用于脊椎X射线异常的分类和定位。这个框架的目的在于开发和评价一个叫做VinDr-SpineXR的深层次学习框架,用于对脊椎X射线异常进行分类和定位。首先,我们建立了一个大型数据集,由来自5 000项研究的10,46868个脊椎X射线图像组成,每个图像由拥有13类异常发现框的有经验的放射科医生手动附加说明。然后,我们培训一个深层次的学习分类师级分类师,以确定脊椎扫描仪扫描仪是否异常,一个探测器将总共7项关键调查结果本地化。 Vindr-SpinexXR在一组试验中评估了2,该模型的准确度。