Atlas-based deformable mutual population segmentation

Aristeidis Sotiras, Nikos Komodakis, Georg Langs, Nikos Paragios

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

5 Scopus citations

Abstract

Segmentation is one of the most critical problems in medical imaging. State-of-the art methods often are based on prior knowledge that can either encode geometry, appearance or both. Despite enormous work in the field, the mainstream is based on the concept of case-tocase segmentation. In this paper, we introduce the notion of mutual population segmentation using discrete optimization where results from a given example influence results for the rest of the examples towards improving the overall segmentation performance. The aim is to combine prior knowledge along with consistency through the simultaneous segmentation of the whole population. This is achieved through their mutual deformation towards the atlas, while being constrained through a simultaneous all-to-all deformable diffeomorphic registration. Promising results demonstrate the potentials of the method.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2009
PublisherIEEE Computer Society
Pages5-8
Number of pages4
ISBN (Print)9781424439324
DOIs
StatePublished - Jan 1 2009
Externally publishedYes
Event2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Conference

Conference2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
CountryUnited States
CityBoston, MA
Period06/28/0907/1/09

Keywords

  • Atlas based segmentation
  • Chest radiographs
  • Lung field segmentation
  • Markov random fields

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