Abstract
The computer-aided diagnosis technology needs to determine whether the bone image is abnormal and needs to locate the location of the lesions accurately. However, there are few publicly available detection datasets of bone lesions. Therefore, for the first, this paper proposes the abnormal detection dataset MURA-objects, which is relabeled based on the large-scale radioactive bone image dataset Musculoskeletal Radiograph. MURA-objects consist of 8431 images, including 7579 in the training set and 852 in the validation set. There are 8933 metal objects and 740 fracture objects in the training images and 1107 metal objects and 124 fracture objects in the validation images. We also give the baseline results using state-of-the-art methods such as faster RCNN, SSD, and YOLOv3, which lays a foundation for future bone imaging lesion detection research. The MURA-objects dataset can be found at https://github.com/wangxin1216/MURA-Objects.
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This study was supported by the National Natural Science Foundation of China (NSFC) under Grant no. 61563039.
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Shao, Y., Wang, X. MURA-objects: a radioactive bone imaging lesion detection dataset. Machine Vision and Applications 33, 96 (2022). https://doi.org/10.1007/s00138-022-01347-1
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DOI: https://doi.org/10.1007/s00138-022-01347-1