ArticleRadiology. Artificial intelligence2020
Automated Myocardial T2 and Extracellular Volume Quantification in Cardiac MRI Using Transfer Learning-based Myocardium Segmentation.
Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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
17 citing papers in PubMed.
- Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection.European radiology · 2026Article
- Breaking barriers: noninvasive AI model for BRAFInternational journal of computer assisted radiology and surgery · 2025Article
- Role of Artificial Intelligence in Congenital Heart Disease and Interventions.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Advanced Myocardial MRI Tissue Characterization Combining Contrast Agent-Free T1-Rho Mapping With Fully Automated Analysis.Journal of magnetic resonance imaging : JMRI · 2025Article
- Scanner-Independent MyoMapNet for Accelerated Cardiac MRI TJournal of magnetic resonance imaging : JMRI · 2024Article
- Present and Future Innovations in AI and Cardiac MRI.Radiology · 2024Review
- 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
- Magnetic resonance myocardial T1ρ mapping : Technical overview, challenges, emerging developments, and clinical applications.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Review
- Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease.Medical sciences (Basel, Switzerland) · 2023Review
- Automated Measurement of Native T1 and Extracellular Volume Fraction in Cardiac Magnetic Resonance Imaging Using a Commercially Available Deep Learning Algorithm.Korean journal of radiology · 2022Article
- A Deep Learning Segmentation Pipeline for Cardiac T1 Mapping Using MRI Relaxation-based Synthetic Contrast Augmentation.Radiology. Artificial intelligence · 2022Article
- Impact of deep learning architectures on accelerated cardiac TNMR in biomedicine · 2022Article
- Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- Accelerated cardiac TJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- The Road Toward Reproducibility of Parametric Mapping of the Heart: A Technical Review.Frontiers in cardiovascular medicine · 2022Review
- Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning.European radiology · 2021Article
- Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot.Pediatric cardiology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeTo assess the performance of an automated myocardial T2 and extracellular volume (ECV) quantification method using transfer learning of a fully convolutional neural network (CNN) pretrained to segment the myocardium on T1 mapping images. MATERIALS AND
methodsA single CNN previously trained and tested using 11 550 manually segmented native T1-weighted images was used to segment the myocardium for automated myocardial T2 and ECV quantification. Reference measurements from 1525 manually processed T2 maps and 1525 ECV maps (from 305 patients) were used to evaluate the performance of the pretrained network. Correlation coefficient
resultsT2 and ECV measurements using the pretrained CNN strongly correlated with reference values in per-patient (T2:
conclusionTransfer learning extends the utility of pretrained CNN-based automated native T1 mapping analysis to T2 and ECV mapping without compromising performance.
Identifiers
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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.