A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors
Abstract Primary malignant bone tumors are the third highest cause of cancer-related mortality among patients under the age of 20. X-ray scan is the primary tool for detecting bone tumors. However, due to the varying morphologies of bone tumors, it is challenging for radiologists to make a definitiv...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04311-y |
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| Summary: | Abstract Primary malignant bone tumors are the third highest cause of cancer-related mortality among patients under the age of 20. X-ray scan is the primary tool for detecting bone tumors. However, due to the varying morphologies of bone tumors, it is challenging for radiologists to make a definitive diagnosis based on radiographs. With the recent advancement in deep learning algorithms, there is a surge of interest in computer-aided diagnosis of primary bone tumors. Nonetheless, the development in this field has been hindered by the lack of publicly available X-ray datasets for bone tumors. To tackle this challenge, we established the Bone Tumor X-ray Radiograph dataset (termed BTXRD) in collaboration with multiple medical institutes and hospitals. The BTXRD dataset comprises 3,746 bone images (1,879 normal and 1,867 tumor), with clinical information and global labels available for each image, and distinct mask and annotated bounding box for each tumor instance. This publicly available dataset can support the development and evaluation of deep learning algorithms for the diagnosis of primary bone tumors. |
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| ISSN: | 2052-4463 |