ArticleIEEE journal of biomedical and health informatics2018
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.
Article in IEEE journal of biomedical and health informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Unveiling sex dimorphism in the healthy cardiac anatomy: Fundamental differences between male and female heart shapes.The Journal of physiology · 2026Article
- MorphiNet: A Graph Subdivision Network for Adaptive Bi-Ventricle Surface Reconstruction.IEEE transactions on medical imaging · 2026Article
- 3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis.Medical & biological engineering & computing · 2025Article
- A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.Nature machine intelligence · 2025Article
- SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects.Medical image analysis · 2024Article
- Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Article
- MRXCAT2.0: Synthesis of realistic numerical phantoms by combining left-ventricular shape learning, biophysical simulations and tissue texture generation.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Multi-Ethnic Study of Atherosclerosis: Relationship between Left Ventricular Shape at Cardiac MRI and 10-year Outcomes.Radiology · 2023Article
- Analysing functional implications of differences in left ventricular morphology using statistical shape modelling.Scientific reports · 2022Article
- Regional left ventricle scar detection from routine cardiac computed tomography angiograms using latent space classification.Computers in biology and medicine · 2022Article
- Understanding and Improving Risk Assessment After Myocardial Infarction Using Automated Left Ventricular Shape Analysis.JACC. Cardiovascular imaging · 2022Article
- Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Interpretable cardiac anatomy modeling using variational mesh autoencoders.Frontiers in cardiovascular medicine · 2022Article
- The impact of shape uncertainty on aortic-valve pressure-drop computations.International journal for numerical methods in biomedical engineering · 2021Article
- Statistical shape analysis of the left atrial appendage predicts stroke in atrial fibrillation.The international journal of cardiovascular imaging · 2021Article
- Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening.Medical & biological engineering & computing · 2021Article
- On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis.Bioengineering (Basel, Switzerland) · 2021Article
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Authors and funding
36 authors.
Funding
Abstract
Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1.
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