ArticleJournal of cardiovascular translational research2022
Validation of a Whole Heart Segmentation from Computed Tomography Imaging Using a Deep-Learning Approach.
Article in Journal of cardiovascular translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- A fully automated explainable predictive model for diagnosing pre-capillary and post-capillary pulmonary hypertension on routine unenhanced CT: results from the ASPIRE registry.European heart journal. Digital health · 2026Article
- Deep learning enables fully automated cineCT-based assessment of regional right ventricular function.European heart journal. Imaging methods and practice · 2026Article
- Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.Journal of the Chinese Medical Association : JCMA · 2024Article
- Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis.Reviews in cardiovascular medicine · 2024Review
- Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging.Frontiers in cardiovascular medicine · 2024Review
- The auto segmentation for cardiac structures using a dual-input deep learning network based on vision saliency and transformer.Journal of applied clinical medical physics · 2022Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study is to develop an automated deep-learning-based whole heart segmentation of ECG-gated computed tomography data. After 21 exclusions, CT acquired before transcatheter aortic valve implantation in 71 patients were reviewed and randomly split in a training (n = 55 patients), validation (n = 8 patients), and a test set (n = 8 patients). A fully automatic deep-learning method combining two convolutional neural networks performed segmentation of 10 cardiovascular structures, which was compared with the manually segmented reference by the Dice index. Correlations and agreement between myocardial volumes and mass were assessed. The algorithm demonstrated high accuracy (Dice score = 0.920; interquartile range: 0.906-0.925) and a low computing time (13.4 s, range 11.9-14.9). Correlations and agreement of volumes and mass were satisfactory for most structures. Six of ten structures were well segmented. Deep-learning-based method allowed automated WHS from ECG-gated CT data with a high accuracy. Challenges remain to improve right-sided structures segmentation and achieve daily clinical application.
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Registered trials
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