Jimenez-Sanchez A, Kazi A, Albarqouni S, Kirchhoff C, Biberthaler P, Navab N, Kirchhoff S, Mateus D (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 15
Pages Range: 847-857
Journal Issue: 5
DOI: 10.1007/s11548-020-02150-x
Purpose: Demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification. The proposed framework aims to improve patient treatment planning and provide support for the training of trauma surgeon residents. Material and methods: A database of 1347 clinical radiographic studies was collected. Radiologists and trauma surgeons annotated all fractures with bounding boxes and provided a classification according to the AO standard. In all experiments, the dataset was split patient-wise in three with the ratio 70%:10%:20% to build the training, validation and test sets, respectively. ResNet-50 and AlexNet architectures were implemented as deep learning classification and localization models, respectively. Accuracy, precision, recall and F
APA:
Jimenez-Sanchez, A., Kazi, A., Albarqouni, S., Kirchhoff, C., Biberthaler, P., Navab, N.,... Mateus, D. (2020). Precise proximal femur fracture classification for interactive training and surgical planning. International Journal of Computer Assisted Radiology and Surgery, 15(5), 847-857. https://doi.org/10.1007/s11548-020-02150-x
MLA:
Jimenez-Sanchez, Amelia, et al. "Precise proximal femur fracture classification for interactive training and surgical planning." International Journal of Computer Assisted Radiology and Surgery 15.5 (2020): 847-857.
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