ArticleProceedings of SPIE--the International Society for Optical Engineering2020
Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 9 citations in OpenAlex.
- Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network.International journal of environmental research and public health · 2022Trial
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- Cardiovascular magnetic resonance semi-automated threshold-based post-processing of right ventricular volumes in repaired tetralogy of Fallot.La Radiologia medica · 2024Article
- Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Cardiac magnetic resonance (CMR) imaging is considered the standard imaging modality for volumetric analysis of the right ventricle (RV), an especially important practice in the evaluation of heart structure and function in patients with repaired Tetralogy of Fallot (rTOF). In clinical practice, however, this requires time-consuming manual delineation of the RV endocardium in multiple 2-dimensional (2D) slices at multiple phases of the cardiac cycle. In this work, we employed a U-Net based 2D convolutional neural network (CNN) classifier in the fully automatic segmentation of the RV blood pool. Our dataset was comprised of 5,729 short-axis cine CMR slices taken from 100 individuals with rTOF. Training of our CNN model was performed on images from 50 individuals while validation was performed on images from 10 individuals. Segmentation results were evaluated by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Use of the CNN model on our testing group of 40 individuals yielded a median DSC of 90% and a median 95
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Registered trials
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