Automated Hip Knee Ankle Angle Determination using Convolutional Neural Networks

  • Advanced osteoarthritis is a leading cause of knee replacement and loss of functionality. Early detection of risk factors plays an important role in the application of preventive measures. One of the risk factors is the leg alignment which influences the speed of knee cartilage degradation. The ’gold standard’ measurement of leg alignment is done by determining the Hip Knee Ankle (HKA) angle from full lower limb radiographs. Convolutional Neural Networks (CNNs) have gained popularity recently in computer vision. In this thesis we developed methods using CNNs to determine HKA angles from full lower limb radiographs. We trained the CNNs using data from the Osteoarthritis Initiative (OAI). We evaluated our method’s performance by evaluating its agreement to experts measurement and its reliability. Our best performing method shows excellent agreement and reliability levels.

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Metadaten
Author:Henok Hagos Gidey
Document Type:Master's Thesis
Granting Institution:Otto-von-Guericke-Universität Magdeburg
Advisor:Felix Ambellan, Alexander Tack, Stefan Zachow
Date of final exam:2019/03/02
Year of first publication:2019
Pagenumber:98