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Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-Beam CT

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Zusammenfassung

Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis. A precise segmentation of the cartilage is a necessary prerequisite for this analysis. This segmentation task has mainly been addressed in Magnetic Resonance Imaging, and was rarely investigated on contrast-enhanced Computed Tomography, where contrast agent visualizes the border between femoral and tibial cartilage. To overcome the main drawback of manual segmentation, namely its high time investment, we propose to use a 3D Convolutional Neural Network for this task. The presented architecture consists of a V-Net with SeLu activation, and a Tversky loss function. Due to the high imbalance between very few cartilage pixels and many background pixels, a high false positive rate is to be expected. To reduce this rate, the two largest segmented point clouds are extracted using a connected component analysis, since they most likely represent the medial and lateral tibial cartilage surfaces. The resulting segmentations are compared to manual segmentations, and achieve on average a recall of 0.69, which confirms the feasibility of this approach.

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Author information

Authors and Affiliations

  1. Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Deutschland

    Jennifer Maier, Luis Carlos Rivera Monroy, Christopher Syben & Andreas Maier

  2. Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Deutschland

    Jennifer Maier

  3. College of Engineering, Ewha Womans University, Seoul, Korea

    Yejin Jeon & Jang-Hwan Choi

  4. Stanford University, Stanford, California, USA

    Mary Elizabeth Hall, Marc Levenston & Garry Gold

  5. Siemens Healthcare GmbH, Erlangen, Deutschland

    Rebecca Fahrig

Authors
  1. Jennifer Maier

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  2. Luis Carlos Rivera Monroy

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  3. Christopher Syben

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  4. Yejin Jeon

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  5. Jang-Hwan Choi

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  6. Mary Elizabeth Hall

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  7. Marc Levenston

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  8. Garry Gold

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  9. Rebecca Fahrig

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  10. Andreas Maier

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Corresponding author

Correspondence toJennifer Maier.

Editor information

Editors and Affiliations

  1. Institut für Medizinische Informatik, Charité - Universitätsmedizin Berlin, Berlin, Germany

    Thomas Tolxdorff

  2. Peter L. Reichertz Institut für Medizinische Informatik, Technische Universität Braunschweig, Braunschweig, Germany

    Thomas M. Deserno

  3. Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Germany

    Heinz Handels

  4. Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität, Erlangen, Germany

    Andreas Maier

  5. Medical Image Computing, E230, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

    Klaus H. Maier-Hein

  6. Fakultät für Informatik und Mathematik, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany

    Christoph Palm

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© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Maier, J.et al. (2020). Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-Beam CT. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_14

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