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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| Format: | Article |
| Language: | English |
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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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| author | Shunhan Yao Yuanxiang Huang Xiaoyu Wang Yiwen Zhang Ian Costa Paixao Zhikang Wang Charla Lu Chai Hongtao Wang Dinggui Lu Geoffrey I Webb Shanshan Li Yuming Guo Qingfeng Chen Jiangning Song |
| author_facet | Shunhan Yao Yuanxiang Huang Xiaoyu Wang Yiwen Zhang Ian Costa Paixao Zhikang Wang Charla Lu Chai Hongtao Wang Dinggui Lu Geoffrey I Webb Shanshan Li Yuming Guo Qingfeng Chen Jiangning Song |
| author_sort | Shunhan Yao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-891a35c8aade466bbd590bbe5841bad8 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-891a35c8aade466bbd590bbe5841bad82025-01-19T12:10:05ZengNature PortfolioScientific Data2052-44632025-01-0112111010.1038/s41597-024-04311-yA Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone TumorsShunhan Yao0Yuanxiang Huang1Xiaoyu Wang2Yiwen Zhang3Ian Costa Paixao4Zhikang Wang5Charla Lu Chai6Hongtao Wang7Dinggui Lu8Geoffrey I Webb9Shanshan Li10Yuming Guo11Qingfeng Chen12Jiangning Song13Medical College, Guangxi UniversitySchool of Computer, Electronic and Information, Guangxi UniversityBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash UniversitySchool of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda RoadBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash UniversityBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash UniversityBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash UniversityBone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Traumatology, The Affiliated Hospital of Youjiang Medical University for NationalitiesDepartment of Data Science and AI, Faculty of Information Technology, Monash UniversitySchool of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda RoadSchool of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda RoadSchool of Computer, Electronic and Information, Guangxi UniversityBiomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash UniversityAbstract 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.https://doi.org/10.1038/s41597-024-04311-y |
| spellingShingle | Shunhan Yao Yuanxiang Huang Xiaoyu Wang Yiwen Zhang Ian Costa Paixao Zhikang Wang Charla Lu Chai Hongtao Wang Dinggui Lu Geoffrey I Webb Shanshan Li Yuming Guo Qingfeng Chen Jiangning Song A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors Scientific Data |
| title | A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors |
| title_full | A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors |
| title_fullStr | A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors |
| title_full_unstemmed | A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors |
| title_short | A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors |
| title_sort | radiograph dataset for the classification localization and segmentation of primary bone tumors |
| url | https://doi.org/10.1038/s41597-024-04311-y |
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