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

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 T1mapping - a quantitative technique for myocardial tissue characterization. The framework achieved nearperfect agreement with expert image analysts in estimating myocardial T1 value ( r = 0 . 987 , p < . 0 0 05 ; mean absolute error (MAE) = 11.3ms), with accurate segmentation quality prediction (Dice coefficient prediction MAE = 0.0339) and classification (accuracy = 0.99), and a fast average processing time of 0.39 second/image. In summary, the QCD framework can generate high-throughput automated image analysis with speed and accuracy that is highly desirable for large-scale clinical applications. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

The proposed quality control-driven segmentation framework integrates neural networks for image analysis and quality control, achieving high-throughput automated image analysis with speed and accuracy, which is highly desirable for large-scale clinical applications.