Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping

•Quality control-driven framework for cardiac segmentation and quality control.•Exploiting variability within deep neural network ensemble to estimate uncertainty.•Novel on-the-fly selection mechanism for the final optimal segmentation.•Accurate, reliable, and fully automated analysis of T1 map with...

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Bibliographic Details
Published in:Medical image analysis 2021-07, Vol.71, p.102029-102029, Article 102029
Main Authors: Hann, Evan, Popescu, Iulia A., Zhang, Qiang, Gonzales, Ricardo A., Barutçu, Ahmet, Neubauer, Stefan, Ferreira, Vanessa M., Piechnik, Stefan K.
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Artificial neural networks
Automation
Cardiovascular MRI
Ensemble neural network
Image analysis
Image processing
Image quality
Image quality assessment
Image segmentation
Inspection
Magnetic resonance imaging
Mapping
Medical imaging
Neural networks
Quality control
Segmentation
Therapeutic applications
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Summary:•Quality control-driven framework for cardiac segmentation and quality control.•Exploiting variability within deep neural network ensemble to estimate uncertainty.•Novel on-the-fly selection mechanism for the final optimal segmentation.•Accurate, reliable, and fully automated analysis of T1 map with visualization.•Highlighting a potential flaw of the Pearson correlation to evaluate quality score. [Display omitted] Recent developments in artificial intelligence have generated increasing interest to deploy automated image analysis for diagnostic imaging and large-scale clinical applications. However, inaccuracy from automated methods could lead to incorrect conclusions, diagnoses or even harm to patients. Manual inspection for potential inaccuracies is labor-intensive and time-consuming, hampering progress towards fast and accurate clinical reporting in high volumes. To promote reliable fully-automated image analysis, we propose a quality control-driven (QCD) segmentation framework. It is an ensemble of neural networks that integrate image analysis and quality control. The novelty of this framework is the selection of the most optimal segmentation based on predicted segmentation accuracy, on-the-fly. Additionally, this framework visualizes segmentation agreement to provide traceability of the quality control process. In this work, we demonstrated the utility of the framework in cardiovascular magnetic resonance T1-mapping - a quantitative technique for myocardial tissue characterization. The framework achieved near-perfect agreement with expert image analysts in estimating myocardial T1 value (r=0.987,p
ISSN:1361-8415
1361-8423
DOI:10.1016/j.media.2021.102029