Abstract
Automatic segmentation of the femur from computed tomography (CT) bodies is a critical and challenging task for computer-aided diagnosis in orthopedic surgery. A femur segmentation method based on improved U-Net is proposed to address the problems of missed detection, false detection and low segmentation accuracy in the femur region due to the small size of the proximal femoral region and the ineffectiveness of U-Net in extracting small target femur features, etc. The method introduces a residual module and an attention mechanism in the U-Net network, which enhances the features of small target femurs. Experimental results on the dataset provided by the PLA General Hospital show that the Intersection over Union (IoU), the check-all rate (Recall), the check-accuracy rate (Precision), and the F-score achieved using this method are 86.02%, 91.73%, 89.42%, and 90.73%, respectively. Compared with the existing semantic segmentation networks U-Net, ResNet, SegNet, etc., this method can focus more on the segmentation effect of small target femurs without affecting the segmentation of large target femurs, thus improving the overall segmentation performance capability of femur images.
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Zhao, J., Han, J., Li, J., Du, G. (2022). Improved U-Net Network for Segmentation on Femur Images. In: Xie, Q., Zhao, L., Li, K., Yadav, A., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-030-89698-0_6
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