This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns the detection and classification of rib fractures on computed tomography (CT) scans and was previously introduced and described by Jin, L. et al (2020). The original dataset was modified to classify center-cropped rib fractures and no images or patient information were added. Data was restructured in compliance with the AUC23 challenge format. Images are 3D tensors: 0: 3D center-cropped CT scan Fracture classification labels: 0: Displaced 1: Non-displaced 2: Buckle 3: Segmental Folder structure: imagesTr (root folder with all patients and studies) ├── ribfrac_0001001_0000.mha (3D CT for fracture 0001001) ├── ribfrac_0001002_0000.mha (3D CT for fracture 0001002) ├── ... Please cite the following article if you are using the Rib Fracture Detection and Classification dataset: Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106

{"references": ["Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020).DOI: https://doi.org/10.1016/j.ebiom.2020.103106"]}