Unsupervised segmentation of stents corrupted by artifacts in medical X-ray images

Hugo Gangloff, Emmanuel Monfrini, Christophe Collet, Nabil Chakfe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a new methodology for the segmentation of stents in 3D X-ray acquisitions. Such data are often corrupted by strong artifacts around the stent, requiring the development of a robust algorithm: because of the medical application, we need to produce an accurate segmentation. Moreover, we aim at developping a robust technique that can handle heterogeneous data. We propose a two-step, coarse-to-fine approach, that handles the corrupted cases. This approach leads to better results illustrated in the context of metallic artefact reduction.

Original languageEnglish (US)
Title of host publication2020 10th International Conference on Image Processing Theory, Tools and Applications, IPTA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728187501
DOIs
StatePublished - Nov 9 2020
Event10th International Conference on Image Processing Theory, Tools and Applications, IPTA 2020 - Virtual, Paris, France
Duration: Nov 9 2020Nov 12 2020

Publication series

Name2020 10th International Conference on Image Processing Theory, Tools and Applications, IPTA 2020

Conference

Conference10th International Conference on Image Processing Theory, Tools and Applications, IPTA 2020
CountryFrance
CityVirtual, Paris
Period11/9/2011/12/20

Keywords

  • image segmentation
  • metal artifacts
  • probabilistic and statistical models
  • X-ray imaging

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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