Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
Published Date: Mar 12, 2019

Abstract

PURPOSE: We present an automated method for extracting anatomical parameters from biplanar radiographs of the spine, which is able to deal with a wide scenario of conditions, including sagittal and coronal deformities, degenerative phenomena as well as images acquired with different fields of view.

Authors

  • Fabio Galbusera
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy. fabio.galbusera@grupposandonato.it.
  • Frank Niemeyer
    Institute of Orthopedic Research and Biomechanics, Center for Trauma Research Ulm, Ulm University, Ulm, Germany.
  • Hans-Joachim Wilke
    Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, University Hospital Ulm, Helmholtzstraße 14, Ulm 89081, Germany. Electronic address: hans-joachim.wilke@uni-ulm.de.
  • Tito Bassani
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy.
  • Gloria Casaroli
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy.
  • Carla Anania
    Department of Neurosurgery, Humanitas Research Hospital, Rozzano, Italy.
  • Francesco Costa
    Department of Neurosurgery, Humanitas Research Hospital, Rozzano, Italy.
  • Marco Brayda-Bruno
    Department of Spine Surgery III, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
  • Luca Maria Sconfienza
    Unit of Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.

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