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Graphical Modeling of Ultrasound Propagation in Tissue for Automatic Bone Segmentation

Firat Ozdemir, Ece Ozkan, Orcun Goksel
Medical Image Computing and Computer Assisted Intervention (MICCAI) 2016
Athens, Greece, October 2016


Bone surface identification and localization in ultrasound have been widely studied in the contexts of computer-assisted orthopedic surgeries, trauma diagnosis, and post-operative follow-up. Nevertheless, the (semi-)automatic bone surface segmentation methods proposed so far either require manual interaction or complex parametrizations, while failing to deliver accuracy fit for clinical purposes. In this paper, we utilize the physics of ultrasound propagation in human tissue by encoding this in a factor graph formulation for an automatic bone surface segmentation approach. We comparatively evaluate our method on annotated in-vivo ultrasound images of bones from several anatomical locations. Our method yields a root-mean-square error of 0.59\,mm, far superior to state-of-the-art approaches.

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  author = {Firat Ozdemir and Ece Ozkan and Orcun Goksel},
  title = {Graphical Modeling of Ultrasound Propagation in Tissue for Automatic Bone Segmentation},
  booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI) 2016},
  year = {2016},
  month = {October},
  pages = {256-264},
  volume = { 9901},
  editor = {S. Ourselin and L. Joskowicz and M. Sabuncu and G. Unal and W. Wells},
  series = {Lecture Notes in Computer Science (LNCS)},
  publisher = {Springer},
  keywords = {}