Estimating Achilles Tendon Healing Progress with Convolutional Neural Networks

Norbert Kapinski , Jakub Zielinski , Bartosz A. Borucki , Tomasz Trzciński , Beata Ciszkowska-Lyson , Krzysztof S. Nowinski

Abstract

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.
Author Norbert Kapinski
Norbert Kapinski,,
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, Jakub Zielinski
Jakub Zielinski,,
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, Bartosz A. Borucki
Bartosz A. Borucki,,
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, Tomasz Trzciński (FEIT / IN)
Tomasz Trzciński,,
- The Institute of Computer Science
, Beata Ciszkowska-Lyson
Beata Ciszkowska-Lyson,,
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, Krzysztof S. Nowinski
Krzysztof S. Nowinski,,
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Pages949-957
Publication size in sheets0.5
Book Frangi Alejandro F., Schnabel Julia A., Davatzikos Christos, Alberola-López Carlos, Fichtinger Gabor (eds.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 21st International Conference, Proceedings, Part II, Lecture Notes In Computer Science, vol. 11071, 2018, Springer Nature Switzerland, ISBN 978-3-030-00933-5, [978-3-030-00934-2], 964 p., DOI:10.1007/978-3-030-00934-2
Keywords in EnglishAchilles tendon trauma, Deep learning, MRI
DOIDOI:10.1007/978-3-030-00934-2_105
URL https://link.springer.com/chapter/10.1007%2F978-3-030-00934-2_105
Languageen angielski
File
10.1007_978-3-030-00934-2_105.pdf 1.29 MB
Score (nominal)15
ScoreMinisterial score = 15.0, 08-10-2018, BookChapterSeriesAndMatConf
Ministerial score (2013-2016) = 15.0, 08-10-2018, BookChapterSeriesAndMatConf
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