Virtual Bone Shape AgingDownload PDF

Apr 09, 2021 (edited May 29, 2021)MIDL 2021 Conference Short SubmissionReaders: Everyone
  • Keywords: Bone Shape, Osteoarthritis, Spherical Encoding
  • TL;DR: We use deep learning to age knee bone surfaces four years.
  • Abstract: We use deep learning to age knee bone surfaces four years. We propose to encode an MRI-based bone surface in a spherical coordinate format, and use these spherical maps to predict shape changes in a 48 months time frame, in subjects with and without osteoarthritis. The experiments show that a 2D V-Net can predict bone surface shape with a mean absolute error of about 1 mm. Our code is available at https://github.com/fcaliva/Bone_Shape_Virtual_Aging.
  • Paper Type: both
  • Source Latex: zip
  • Primary Subject Area: Detection and Diagnosis
  • Secondary Subject Area: Application: Radiology
  • Paper Status: original work, not submitted yet
  • Source Code Url: The code is available at https://github.com/fcaliva/Bone_Shape_Virtual_Aging/
  • Data Set Url: The Osteoarthritis Initiative (OAI) is a multi-center, ten-year observational study of men and women, sponsored by the National Institutes of Health (part of the Department of Health and Human Services). The goals of the OAI are to provide resources to enable a better understanding of prevention and treatment of knee osteoarthritis, one of the most common causes of disability in adults. https://nda.nih.gov/oai/
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  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
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