Standardized evaluation methodology and reference database for evaluating IVUS image segmentation

Simone Balocco, Carlo Gatta, Francesco Ciompi, Andreas Wahle, Petia Radeva, Stephane Carlier, Gozde Unal, Elias Sanidas, Josepa Mauri, Xavier Carillo, Tomas Kovarnik, Ching Wei Wang, Hsiang Chou Chen, Themis P. Exarchos, Dimitrios I. Fotiadis, François Destrempes, Guy Cloutier, Oriol Pujol, Marina Alberti, E. Gerardo Mendizabal-RuizMariano Rivera, Timur Aksoy, Richard W. Downe, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

121 Scopus citations

Abstract

This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated.We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved.

Original languageEnglish (US)
Pages (from-to)70-90
Number of pages21
JournalComputerized Medical Imaging and Graphics
Volume38
Issue number2
DOIs
StatePublished - Mar 2014

Keywords

  • Algorithm comparison
  • Evaluation framework
  • IVUS (intravascular ultrasound)
  • Image segmentation

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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