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Fully Automatic Planning of Total Shoulder Arthroplasty Without Segmentation: A Deep Learning Based Approach

  • Paul Kulyk
  • Lazaros Vlachopoulos
  • Philipp Fürnstahl
  • Guoyan Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11404)

Abstract

We present a method for automatically determining the position and orientation of the articular marginal plane (AMP) of the proximal humerus in computed tomography (CT) images without segmentation or hand-crafted features. The process is broken down into 3 stages. Stage 1 determines a coarse estimation of the AMP center by sampling patches over the entire image and combining predictions with a novel kernel density estimation method. Stage 2 utilizes the estimate from stage 1 to focus on a smaller sampling region and operates at a higher images resolution to obtain a refined prediction of the AMP center. Stage 3 focuses patch sampling on the region around the center obtained at stage 2 and regresses the tip of a vector normal to the AMP which yields the orientation of the plane. The system was trained and evaluated on 27 upper arm CTs. In a 4-fold cross-validation the mean error in estimating the AMP center was \(1.30\,{\pm }\,0.65\) mm and the angular error for estimating the normal vector was \(4.68\,{\pm }\,2.84^\circ \).

Keywords

Regression Proximal humerus Articular marginal plane Deep learning Total shoulder arthroplasty 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paul Kulyk
    • 1
    • 2
  • Lazaros Vlachopoulos
    • 3
  • Philipp Fürnstahl
    • 3
  • Guoyan Zheng
    • 1
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.College of MedicineUniversity of SaskatchewanSaskatoonCanada
  3. 3.Computer Assisted Research and Development GroupUniversity of Zurich, Balgrist University HospitalZurichSwitzerland

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