ArticleComputer methods and programs in biomedicine2023
An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar.
Article in Computer methods and programs in biomedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 22 citations in OpenAlex.
- Review
- From Echocardiography to CT/MRI: Lessons for AI Implementation in Cardiovascular Imaging in LMICs-A Systematic Review and Narrative Synthesis.Bioengineering (Basel, Switzerland) · 2025Review
- Assisting clinical diagnosis with interpretable fuzzy probabilistic modelling.BMC medical informatics and decision making · 2025Article
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- Diagnosing acute myocarditis in the emergency department-advancing cardiac MRI with a focus on low-field MR applications.Frontiers in radiology · 2025Review
- Advancements and applications of artificial intelligence in cardiovascular imaging: a comprehensive review.European heart journal. Imaging methods and practice · 2024Review
- Automated cardiovascular MR myocardial scar quantification with unsupervised domain adaptation.European radiology experimental · 2024Article
- Imaging for the diagnosis of acute myocarditis: can artificial intelligence improve diagnostic performance?Frontiers in cardiovascular medicine · 2024Review
- Left Ventricle Detection from Cardiac Magnetic Resonance Relaxometry Images Using Visual Transformer.Sensors (Basel, Switzerland) · 2023Article
- Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.Frontiers in cellular and infection microbiology · 2023Article
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Authors and funding
16 authors at 6 institutions in 3 countries.
Funding
Abstract
BACKGROUND AND
objectivesMyocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.
methodsDB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) models based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo- and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.
resultsThe dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets. Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker (<1 min versus 7 ± 3 min), and required minimal user interaction.
conclusionsOur solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.
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