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Multi-atlas Segmentation and Landmark Localization in Images with Large Field of View

  • Tobias Gass
  • Gabor Szekely
  • Orcun Goksel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8848)

Abstract

In this work, we present multi-atlas based techniques for both segmentation and landmark detection in images with large field-of-view (FOV). Such images can provide important insight in the anatomical structure of the human body, but are challenging to deal with since the localization search space for landmarks and organs, in addition to the raw amount of data, is large. In many studies, segmentation and localization techniques are developed specifically for an individual target anatomy or image modality. This can leave a substantial amount of the potential of large FOV images untapped, as the co-localization and shape variability of organs are neglected. We thus focus on modality and anatomy independent techniques to be applied to a wide range of input images. For segmentation, we propagate the multi-organ label maps from several atlases to a target image via a large FOV Markov random field (MRF) based non-rigid registration method. The propagated labels are then fused in the target domain using similarity-weighted majority voting. For landmark localization, we use a consensus based fusion of location estimates from several atlases identified by a template-matching approach. We present our results in the IEEE ISBI 2014 VISCERAL challenge as well as VISCERAL Anatomy1 and Anatomy2 benchmarks.

Keywords

Target Image Markov Random Field Landmark Localization Landmark Detection Weighted Majority Vote 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement n\(^\circ \) 318068. This work has also received funding from the Swiss National Center of Competence in Research on Computer Aided and Image Guided Medical Interventions (NCCR Co-Me) supported by the Swiss National Science Foundation.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Vision LabETH ZurichZurichSwitzerland

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