ArticleMedical image analysis2021
Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed, 77 citations in OpenAlex.
- Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification.Hypertension (Dallas, Tex. : 1979) · 2026Article
- Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection.European radiology · 2026Article
- Myocardial fibrosis and tissue alterations predict cardiovascular outcomes in chronic kidney disease-a prospective virtual twin study design using large-scale population database.Frontiers in cardiovascular medicine · 2026Article
- Cardiac magnetic resonance characteristics and prognostic associations of hypertension-mediated left ventricular hypertrophy.European heart journal. Imaging methods and practice · 2026Article
- A semi-automated quality assurance tool for cardiovascular magnetic resonance imaging: application to outlier detection, artificial intelligence evaluation and trainee feedback.BMC medical informatics and decision making · 2025Article
- Evaluation of Projection Images for Visual Quality Control of Automated Left and Right Lung Segmentations on T1-Weighted MRI in Large-Scale Clinical Cohort Studies.Tomography (Ann Arbor, Mich.) · 2025Article
- NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.Scientific reports · 2025Article
- Sparse scanning encoding and neural network decoding for compressed photoacoustic microscopy.Photoacoustics · 2025Article
- Shaping Quality in Cardiovascular Magnetic Resonance: A Comparative Study of Segmentation Approaches by Trainees and Experts.CJC open · 2025Article
- [Artificial intelligence in cardiovascular radiology : Image acquisition, image reconstruction and workflow optimization].Radiologie (Heidelberg, Germany) · 2024Article
- The Association Between Periodontal Disease and Cardiovascular Disease: Insights From Imaging, Observational, and Genetic Data.JACC. Advances · 2024Article
- Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data.Scientific reports · 2024Article
- Article
- Ventricular volume asymmetry as a novel imaging biomarker for disease discrimination and outcome prediction.European heart journal open · 2024Article
- Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
- Present and Future Innovations in AI and Cardiac MRI.Radiology · 2024Review
- Neuroticism personality traits are linked to adverse cardiovascular phenotypes in the UK Biobank.European heart journal. Cardiovascular Imaging · 2023Article
- Deep Ensembles Are Robust to Occasional Catastrophic Failures of Individual DNNs for Organs Segmentations in CT Images.Journal of digital imaging · 2023Article
- Temporal Uncertainty Localization to Enable Human-in-the-Loop Analysis of Dynamic Contrast-Enhanced Cardiac MRI Datasets.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Validation of a deep learning-based software for automated analysis of T2 mapping in cardiac magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
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<.0005; 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.