Estimating Achilles Tendon Healing Progress with Convolutional Neural Networks

  • Norbert Kapinski
  • Jakub Zielinski
  • Bartosz A. Borucki
  • Tomasz Trzcinski
  • Beata Ciszkowska-Lyson
  • Krzysztof S. Nowinski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Quantitative assessment of a treatment progress in the Achilles tendon healing process - one of the most common musculoskeletal disorders in modern medical practice - is typically a long and complex process: multiple MRI protocols need to be acquired and analysed by radiology experts for proper assessment. In this paper, we propose to significantly reduce the complexity of this process by using a novel method based on a pre-trained convolutional neural network. We first train our neural network on over 500 000 2D axial cross-sections from over 3 000 3D MRI studies to classify MRI images as belonging to a healthy or injured class, depending on the patient’s condition. We then take the outputs of a modified pre-trained network and apply linear regression on the PCA-reduced space of the features to assess treatment progress. Our method allows to reduce up to 5-fold the amount of data needed to be registered during the MRI scan without any information loss. Furthermore, we are able to predict the healing process phase with equal accuracy to human experts in 3 out of 6 main criteria. Finally, contrary to the current approaches to healing assessment that rely on radiologist subjective opinion, our method allows to objectively compare different treatments methods which can lead to faster patient’s recovery.


Achilles tendon trauma Deep learning MRI 



The following work was part of Novel Scaffold-based Tissue Engineering Approaches to Healing and Regeneration of Tendons and Ligaments (START) project, co-funded by The National Centre for Research and Development (Poland) within STRATEGMED programme (STRATEGMED1/233224/10/NCBR/2014).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Norbert Kapinski
    • 1
  • Jakub Zielinski
    • 1
    • 3
  • Bartosz A. Borucki
    • 1
  • Tomasz Trzcinski
    • 2
    • 5
  • Beata Ciszkowska-Lyson
    • 4
  • Krzysztof S. Nowinski
    • 1
  1. 1.University of WarsawWarsawPoland
  2. 2.Warsaw University of TechnologyWarsawPoland
  3. 3.Medical University of WarsawWarsawPoland
  4. 4.Carolina Medical CenterWarsawPoland
  5. 5.TooplooxWrocławPoland

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