Yao Shunhan, Huang Yuanxiang, Wang Xiaoyu, Zhang Yiwen, Paixao Ian Costa, Wang Zhikang, Chai Charla Lu, Wang Hongtao, Lu Dinggui, Webb Geoffrey I, Li Shanshan, Guo Yuming, Chen Qingfeng, Song Jiangning
Medical College, Guangxi University, Nanning, Guangxi, 530000, China.
Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia.
Sci Data. 2025 Jan 16;12(1):88. doi: 10.1038/s41597-024-04311-y.
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.
原发性恶性骨肿瘤是20岁以下患者中与癌症相关死亡率的第三大原因。X线扫描是检测骨肿瘤的主要工具。然而，由于骨肿瘤形态各异，放射科医生很难根据X线片做出明确诊断。随着深度学习算法的最新进展，计算机辅助诊断原发性骨肿瘤引起了人们极大的兴趣。尽管如此，该领域的发展受到缺乏公开可用的骨肿瘤X线数据集的阻碍。为应对这一挑战，我们与多家医学机构和医院合作建立了骨肿瘤X线片数据集（称为BTXRD）。BTXRD数据集包含3746张骨图像（1879张正常图像和1867张肿瘤图像），每张图像都有临床信息和全局标签，每个肿瘤实例都有独特的掩码和标注边界框。这个公开可用的数据集可以支持用于诊断原发性骨肿瘤的深度学习算法的开发和评估。
