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

  • Jennifer MaierEmail author
  • Luis Carlos Rivera Monroy
  • Christopher Syben
  • Yejin Jeon
  • Jang-Hwan Choi
  • Mary Elizabeth Hall
  • Marc Levenston
  • Garry Gold
  • Rebecca Fahrig
  • Andreas Maier
Conference paper
  • 52 Downloads
Part of the Informatik aktuell book series (INFORMAT)

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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Copyright information

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

Authors and Affiliations

  • Jennifer Maier
    • 1
    • 2
    Email author
  • Luis Carlos Rivera Monroy
    • 1
  • Christopher Syben
    • 1
  • Yejin Jeon
    • 3
  • Jang-Hwan Choi
    • 3
  • Mary Elizabeth Hall
    • 4
  • Marc Levenston
    • 4
  • Garry Gold
    • 4
  • Rebecca Fahrig
    • 5
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU)ErlangenDeutschland
  2. 2.Machine Learning and Data Analytics LabFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU)ErlangenDeutschland
  3. 3.College of EngineeringEwha Womans UniversitySeoulKorea
  4. 4.Stanford UniversityStanfordCaliforniaUSA
  5. 5.Siemens Healthcare GmbHErlangenDeutschland

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