Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images.

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation. However, accurate segmentation of the left ventricular (LV) myocardium remains a challenge due to limited training data and lack of quality control. This study addresses these issues by leveraging generative adversarial networks (GAN)-generated virtual native enhancement (VNE) images to expand the training set and incorporating an automated quality control-driven (QCD) framework to improve segmentation reliability.
Methods: A dataset comprising 4,716 LGE images (from 1,363 patients with hypertrophic cardiomyopathy and myocardial infarction) was used for development. To generate additional clinically validated data, LGE data were augmented with a GAN-based generator to produce VNE images. LV was contoured on these images manually by clinical observers. To create diverse candidate segmentations, the QCD framework involved multiple U-Nets, which were combined using statistical rank filters. The framework predicted the Dice Similarity Coefficient (DSC) for each candidate segmentation, with the highest predicted DSC indicating the most accurate and reliable result. The performance of the QCD ensemble framework was evaluated on both LGE and VNE test datasets (309 LGE/VNE images from 103 patients), assessing segmentation accuracy (DSC) and quality prediction (mean absolute error (MAE) and binary classification accuracy).
Results: The QCD framework effectively and rapidly segmented the LV myocardium (<1 s per image) on both LGE and VNE images, demonstrating robust performance on both test datasets with similar mean DSC (LGE: 0.845 ± 0.075 ; VNE: 0.845 ± 0.071 ; p = n s ). Incorporating GAN-generated VNE data into the training process consistently led to enhanced performance for both individual models and the overall framework. The quality control mechanism yielded a high performance ( MAE = 0.043 , accuracy = 0.951 ) emphasising the accuracy of the quality control-driven strategy in predicting segmentation quality in clinical settings. Overall, no statistical difference ( p = n s ) was found when comparing the LGE and VNE test sets across all experiments.
Conclusions: The QCD ensemble framework, leveraging GAN-generated VNE data and an automated quality control mechanism, significantly improved the accuracy and reliability of LGE segmentation, paving the way for enhanced and accountable diagnostic imaging in routine clinical use.
Competing Interests: VF, QZ, and SP have authorship rights for patent WO2021/044153 (“Enhancement of Medical Images”; granted March 11, 2021). EH, IP, VF, QZ and SP have authorship rights for patent WO/2020/161481 (“Method and Apparatus for Quality Prediction”; granted August 13, 2020). SP has patent authorship rights for US patent US20120078084A1 (“Systems and Methods for Shortened Look Locker Inversion Recovery [Sh-MOLLI] Cardiac Gated Mapping of T1”; granted March 15, 2016). DI was employed by Artificio Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(© 2023 Gonzales, Ibáñez, Hann, Popescu, Burrage, Lee, Altun, Weintraub, Kwong, Kramer, Neubauer, Ferreira, Zhang and Piechnik.)