ArticleBioMed research international2019
A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging.
Article in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 26 citations in OpenAlex.
- An Overview of Deep Learning Methods for Left Ventricle Segmentation.Computational intelligence and neuroscience · 2023Review
- Artificial intelligence study on left ventricular function among normal individuals, hypertrophic cardiomyopathy and dilated cardiomyopathy patients using 1.5T cardiac cine MR images obtained by SSFP sequence.The British journal of radiology · 2022Article
- Computational Modeling of Right Ventricular Motion and Intracardiac Flow in Repaired Tetralogy of Fallot.Cardiovascular engineering and technology · 2022Article
- Real-Time Magnetic Resonance Imaging.Journal of magnetic resonance imaging : JMRI · 2022Review
- Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot.Pediatric cardiology · 2021Article
- Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm.Computational and mathematical methods in medicine · 2021Article
- Artificial intelligence in cardiac radiology.La Radiologia medica · 2020Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe purpose of this study was to segment the left ventricle (LV) blood pool, LV myocardium, and right ventricle (RV) blood pool of end-diastole and end-systole frames in free-breathing cardiac magnetic resonance (CMR) imaging. Automatic and accurate segmentation of cardiac structures could reduce the postprocessing time of cardiac function analysis.
methodWe proposed a novel deep learning network using a residual block for the segmentation of the heart and a random data augmentation strategy to reduce the training time and the problem of overfitting. Automated cardiac diagnosis challenge (ACDC) data were used for training, and the free-breathing CMR data were used for validation and testing.
resultsThe average Dice was 0.919 (LV), 0.806 (myocardium), and 0.818 (RV). The average IoU was 0.860 (LV), 0.699 (myocardium), and 0.761 (RV).
conclusionsThe proposed method may aid in the segmentation of cardiac images and improves the postprocessing efficiency of cardiac function analysis.
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Registered trials
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