Standardized evaluation methodology and reference database for evaluating IVUS image segmentation
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem, Research Support, U.S. Gov't, Non-P.H.S.
Grantová podpora
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-zdroje
- Klíčová slova
- Algorithm comparison, Evaluation framework, IVUS (intravascular ultrasound), Image segmentation,
- MeSH
- databáze faktografické normy MeSH
- internacionalita MeSH
- interpretace obrazu počítačem metody normy MeSH
- intervenční ultrasonografie metody normy MeSH
- lidé MeSH
- nemoci koronárních tepen diagnostické zobrazování MeSH
- referenční hodnoty MeSH
- reprodukovatelnost výsledků MeSH
- senzitivita a specificita MeSH
- směrnice pro lékařskou praxi jako téma * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem 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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