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MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
- Source :
- Journal of Cardiovascular Magnetic Resonance, Journal of Cardiovascular Magnetic Resonance, Vol 23, Iss 1, Pp 1-15 (2021)
- Publication Year :
- 2021
- Publisher :
- BioMed Central, 2021.
-
Abstract
- Background Mitral annular plane systolic excursion (MAPSE) and left ventricular (LV) early diastolic velocity (e’) are key metrics of systolic and diastolic function, but not often measured by cardiovascular magnetic resonance (CMR). Its derivation is possible with manual, precise annotation of the mitral valve (MV) insertion points along the cardiac cycle in both two and four-chamber long-axis cines, but this process is highly time-consuming, laborious, and prone to errors. A fully automated, consistent, fast, and accurate method for MV plane tracking is lacking. In this study, we propose MVnet, a deep learning approach for MV point localization and tracking capable of deriving such clinical metrics comparable to human expert-level performance, and validated it in a multi-vendor, multi-center clinical population. Methods The proposed pipeline first performs a coarse MV point annotation in a given cine accurately enough to apply an automated linear transformation task, which standardizes the size, cropping, resolution, and heart orientation, and second, tracks the MV points with high accuracy. The model was trained and evaluated on 38,854 cine images from 703 patients with diverse cardiovascular conditions, scanned on equipment from 3 main vendors, 16 centers, and 7 countries, and manually annotated by 10 observers. Agreement was assessed by the intra-class correlation coefficient (ICC) for both clinical metrics and by the distance error in the MV plane displacement. For inter-observer variability analysis, an additional pair of observers performed manual annotations in a randomly chosen set of 50 patients. Results MVnet achieved a fast segmentation ( Conclusion A dual-stage deep learning approach for automated annotation of MV points for systolic and diastolic evaluation in CMR long-axis cine images was developed. The method is able to carefully track these points with high accuracy and in a timely manner. This will improve the feasibility of CMR methods which rely on valve tracking and increase their utility in a clinical setting.
- Subjects :
- Annotation
Population
Magnetic Resonance Imaging, Cine
Residual
Tracking (particle physics)
Ventricular Function, Left
Tracking error
Predictive Value of Tests
Mitral valve
medicine
Diseases of the circulatory (Cardiovascular) system
Humans
Radiology, Nuclear Medicine and imaging
Segmentation
Computer vision
education
education.field_of_study
Left ventricular dysfunction
Radiological and Ultrasound Technology
Cardiac cycle
Orientation (computer vision)
business.industry
Research
Reproducibility of Results
Magnetic Resonance Imaging
medicine.anatomical_structure
RC666-701
Mitral Valve
Artificial intelligence
Neural Networks, Computer
Cardiology and Cardiovascular Medicine
business
Residual neural networks
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Journal of Cardiovascular Magnetic Resonance, Journal of Cardiovascular Magnetic Resonance, Vol 23, Iss 1, Pp 1-15 (2021)
- Accession number :
- edsair.doi.dedup.....c834ff1d94b6ebd2b3d69bd1faef5c92