Automatic cardiac ventricle segmentation in MR images: a validation study

Autor: Damien Grosgeorge, Jérôme Caudron, Caroline Petitjean, Jeannette Fares, Jean-Nicolas Dacher
Přispěvatelé: Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Service d'imagerie médicale [CHU Rouen], Hôpital Charles Nicolle [Rouen]-CHU Rouen, Normandie Université (NU)
Jazyk: angličtina
Rok vydání: 2010
Předmět:
Databases
Factual
Computer science
02 engineering and technology
030218 nuclear medicine & medical imaging
Pattern Recognition
Automated
Cohort Studies
0302 clinical medicine
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Validation
0202 electrical engineering
electronic engineering
information engineering
Computer vision
Segmentation
Ground truth
Image segmentation
medicine.diagnostic_test
Active contours
Ventricle segmentation
General Medicine
Middle Aged
Computer Graphics and Computer-Aided Design
Computer Science Applications
Pattern recognition (psychology)
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Mr images
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Algorithms
Adult
Validation study
Heart Ventricles
Biomedical Engineering
Magnetic Resonance Imaging
Cine
Health Informatics
Sensitivity and Specificity
03 medical and health sciences
[SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system
Image Interpretation
Computer-Assisted
medicine
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Humans
Radiology
Nuclear Medicine and imaging
Least-Squares Analysis
Aged
business.industry
Cardiac Ventricle
Magnetic resonance imaging
Surgery
Artificial intelligence
business
Cardiac magnetic resonance imaging (CMRI)
Zdroj: International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery, Springer Verlag, 2010, pp.1. ⟨10.1007/s11548-010-0532-6⟩
ISSN: 1861-6410
1861-6429
DOI: 10.1007/s11548-010-0532-6⟩
Popis: International audience; Purpose: Segmenting the cardiac ventricles in magnetic resonance (MR) images is required for cardiac function assessment. Numerous segmentation methods have been developed and applied to MR ventriculography. Quantitative validation of these segmentation methods with ground truth is needed prior to clinical use, but requires manual delineation of hundreds of images. We applied a well-established method to this problem and rigorously validated the results. Methods: An automatic method based on active contours without edges was used for left and the right ventricle cavity segmentation. A large database of 1,920MRimages obtained from 59 patients who gave informed consent was evaluated. Two standard metrics were used for quantitative error measurement. Results Segmentation results are comparable to previously reported values in the literature. Since different points in the cardiac cycle and different slice levelswere used in this study, a detailed error analysis is possible. Better performance was obtained at end diastole than at end systole, and on midventricular slices than apical slices. Localization of segmentation errors were highlighted through a study of their spatial distribution. Conclusions: Ventricular segmentation based on region driven active contours provided satisfactory results in MRI, without the use of a priori knowledge. The study of error distribution allows identification of potential improvements in algorithm performance.
Databáze: OpenAIRE
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