VinDr-SpineXR: An open dataset for spinal lesions detection and classification from radiographs
Radiographs are used as the most important imaging tool for identifying spine anomalies in clinical practice. To the best of our knowledge, no existing studies have been devoted to the development and evaluation of a comprehensive system for classifying and localizing multiple spine lesions from X-ray scans. The lack of large datasets with high-quality images and human experts’ annotations is the key obstacle. To fill this gap, Vingroup Big Data Institute (VinBigdata) has created and made freely available the VinDr-SpineXR: A large-scale X-ray dataset for spinal lesions detection and classification. The VinDr-SpineXR contains 10,469 images from 5,000 studies that are manually annotated with 13 types of abnormalities, each scan was annotated by an expert radiologist.
To the best of our knowledge, the VinDr-SpineXR is currently the largest dataset to date that provides radiologist’s bounding-box annotations for developing supervised-learning object detection algorithms. We believe that the dataset will serve as a benchmark dataset for accelerating the development and evaluation of new machine learning models for the spinal X-ray interpretation.
Table 1. Overview of publicly available MSK image datasets.
Figure 1. Examples of spine X-ray scans with radiologist’s annotations. Abnormal findings (local labels) marked by radiologists are plotted on the original images for visualization purposes.
Table 2. Characteristics of patients in the training and test datasets.
For any publication that explores this resource, the authors must cite this original paper:
Hieu T. Nguyen, Hieu H. Pham, Nghia T. Nguyen, Ha Q. Nguyen, Thang Q. Huynh, Minh Dao, and Van Vu, “VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs,” in Proceedings of the 2021 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021)
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