Standardized evaluation methodology and reference database for evaluating IVUS image segmentation
Language English Country United States Media print-electronic
Document type Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.
Grant support
R01EB004640
NIBIB NIH HHS - United States
R01HL063373
NHLBI NIH HHS - United States
PubMed
24012215
DOI
10.1016/j.compmedimag.2013.07.001
PII: S0895-6111(13)00129-8
Knihovny.cz E-resources
- Keywords
- Algorithm comparison, Evaluation framework, IVUS (intravascular ultrasound), Image segmentation,
- MeSH
- Databases, Factual standards MeSH
- Internationality MeSH
- Image Interpretation, Computer-Assisted methods standards MeSH
- Ultrasonography, Interventional methods standards MeSH
- Humans MeSH
- Coronary Artery Disease diagnostic imaging MeSH
- Reference Values MeSH
- Reproducibility of Results MeSH
- Sensitivity and Specificity MeSH
- Practice Guidelines as Topic * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
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.
2nd Department of Internal Medicine Charles University Prague Czech Republic
Cardiovascular Research Foundation New York USA
Centro de Investigacion en Matematicas Guanajuato Mexico
Computational Biomedicine Lab Department of Computer Science University of Houston Houston TX USA
Computer Vision Center Bellaterra Spain
Department of Electrical and Computer Engineering The University of Iowa Iowa City USA
Dept Matemàtica Aplicada i Anàlisi Universitat de Barcelona Barcelona Spain
Faculty of Engineering and Natural Sciences Sabanci University Turkey
Hospital Universitari Germans Trias i Pujol Badalona Spain
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