Abstract
Deformities of the lower limbs are a common clinical problem encountered in orthopedic practices. Several methods have been proposed for measuring lower limb alignment and joint orientation clinically or using computer-assisted methods. In this work we introduce a new approach for measuring lower limb alignment and joint orientation on the basis of bones segmented by deep neural networks. The bones are segmented on X-ray images using an U-net convolutional neural network. It has been trained on forty manually segmented images. Afterwards, the segmented bones are post-processed using fully connected CRFs. Finally, lines are fitted to pruned skeletons representing the bones. We discuss algorithms for measuring lower limb alignment and joint orientation. We present both qualitative and quantitative segmentation results on ten test images. We compare the results that were obtained manually using a computer-assisted program and by the proposed algorithm.
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Kwolek, K., Brychcy, A., Kwolek, B., Marczyński, W. (2019). Measuring Lower Limb Alignment and Joint Orientation Using Deep Learning Based Segmentation of Bones. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_44
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