Measuring Lower Limb Alignment and Joint Orientation Using Deep Learning Based Segmentation of Bones

• Kamil Kwolek ^[2] ; Adrian Brychcy ^[2] ; Bogdan Kwolek ^[1] ; Wojciech Marczyński ^[3]
  1. [1] AGH University of Science and Technology

     AGH University of Science and Technology

     Kraków, Polonia

     
  2. [2] Professor Adam Gruca Teaching Hospital (Otwock, Poland)
  3. [3] Professor Adam Gruca Teaching Hospital. Centre of Postgraduate Medical Education (Otwock, Poland)

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• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 514-525
• Idioma: inglés
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  • 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.