ArticleFrontiers in cardiovascular medicine2021
Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Multimodal Nomogram for the Prenatal Risk Assessment of Hypoplastic Left Heart Syndrome Using Self-Supervised Learning.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- Biomechanically Informed Image Registration for Patient-Specific Aortic Valve Strain Analysis.Annals of biomedical engineering · 2026Article
- Feasibility of 3D echocardiography-CT/CMR fusion to create atrioventricular valve-integrated 3D printed heart models in complex congenital heart disease: greater incremental benefit for surgeons than cardiac imagers.3D printing in medicine · 2026Article
- Neural Computing Advancements in Cardiac Imaging: A Review of Deep Learning Approaches for Heart Disease Diagnosis.Journal of imaging · 2026Review
- Article
- A noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks.Acta biomaterialia · 2025Article
- Quantitative Analysis of 3D Anatomy to Inform Planning of Ductal Arteriosus Stenting.Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions · 2025Article
- TOWARDS PATIENT-SPECIFIC SURGICAL PLANNING FOR BICUSPID AORTIC VALVE REPAIR: FULLY AUTOMATED SEGMENTATION OF THE AORTIC VALVE IN 4D CT.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Article
- Bayesian Optimization-Based Inverse Finite Element Analysis for Atrioventricular Heart Valves.Annals of biomedical engineering · 2024Article
- Tricuspid Valve Regurgitation in Hypoplastic Left Heart Syndrome: Current Insights and Future Perspectives.Journal of cardiovascular development and disease · 2023Review
- A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography.World journal of pediatric surgery · 2023Article
- The tricuspid valve in hypoplastic left heart syndrome: Echocardiography provides insight into anatomy and function.Frontiers in pediatrics · 2023Review
- Visualization and Quantification of the Unrepaired Complete Atrioventricular Canal Valve Using Open-Source Software.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2022Article
- Commentary: Tricuspid regurgitation in hypoplastic left heart syndrome: Getting beyond a finger in the dyke.JTCVS open · 2022Article
- A pilot investigation of the tricuspid valve annulus in newborns with hypoplastic left heart syndrome.JTCVS open · 2022Article
- Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome.Statistical atlases and computational models of the heart. STACOM (Workshop) · 2022Article
- SlicerHeart: An open-source computing platform for cardiac image analysis and modeling.Frontiers in cardiovascular medicine · 2022Review
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9 authors.
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Abstract
Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect in which the right ventricle and associated tricuspid valve (TV) alone support the circulation. TV failure is thus associated with heart failure, and the outcome of TV valve repair are currently poor. 3D echocardiography (3DE) can generate high-quality images of the valve, but segmentation is necessary for precise modeling and quantification. There is currently no robust methodology for rapid TV segmentation, limiting the clinical application of these technologies to this challenging population. We utilized a Fully Convolutional Network (FCN) to segment tricuspid valves from transthoracic 3DE. We trained on 133 3DE image-segmentation pairs and validated on 28 images. We then assessed the effect of varying inputs to the FCN using Mean Boundary Distance (MBD) and Dice Similarity Coefficient (DSC). The FCN with the input of an annular curve achieved a median DSC of 0.86 [IQR: 0.81-0.88] and MBD of 0.35 [0.23-0.4] mm for the merged segmentation and an average DSC of 0.77 [0.73-0.81] and MBD of 0.6 [0.44-0.74] mm for individual TV leaflet segmentation. The addition of commissural landmarks improved individual leaflet segmentation accuracy to an MBD of 0.38 [0.3-0.46] mm. FCN-based segmentation of the tricuspid valve from transthoracic 3DE is feasible and accurate. The addition of an annular curve and commissural landmarks improved the quality of the segmentations with MBD and DSC within the range of human inter-user variability. Fast and accurate FCN-based segmentation of the tricuspid valve in HLHS may enable rapid modeling and quantification, which in the future may inform surgical planning. We are now working to deploy this network for public use.
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