ArticleBioMed research international2018
A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography.
Article in BioMed research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Multi-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography.IEEE transactions on medical imaging · 2024Article
- Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases.Anatolian journal of cardiology · 2024Article
- Automated detection and localization of pericardial effusion from point-of-care cardiac ultrasound examination.Medical & biological engineering & computing · 2023Article
- Real-time echocardiography image analysis and quantification of cardiac indices.Medical image analysis · 2022Article
- Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography Using Multi-task Learning.Statistical atlases and computational models of the heart. STACOM (Workshop) · 2022Article
- Comparative studies of deep learning segmentation models for left ventricle segmentation.Frontiers in public health · 2022Article
- MITEA: A dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging.Frontiers in cardiovascular medicine · 2022Article
- Automatic Segmentation of Left Ventricle in Echocardiography Based on YOLOv3 Model to Achieve Constraint and Positioning.Computational and mathematical methods in medicine · 2021Article
- Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions.IEEE reviews in biomedical engineering · 2021Review
- Deep Learning for Cardiac Image Segmentation: A Review.Frontiers in cardiovascular medicine · 2020Review
- Paraspinal Muscle Segmentation Based on Deep Neural Network.Sensors (Basel, Switzerland) · 2019Article
- Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
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Authors and funding
6 authors.
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Abstract
Segmentation of the left ventricle (LV) from three-dimensional echocardiography (3DE) plays a key role in the clinical diagnosis of the LV function. In this work, we proposed a new automatic method for the segmentation of LV, based on the fully convolutional networks (FCN) and deformable model. This method implemented a coarse-to-fine framework. Firstly, a new deep fusion network based on feature fusion and transfer learning, combining the residual modules, was proposed to achieve coarse segmentation of LV on 3DE. Secondly, we proposed a method of geometrical model initialization for a deformable model based on the results of coarse segmentation. Thirdly, the deformable model was implemented to further optimize the segmentation results with a regularization item to avoid the leakage between left atria and left ventricle to achieve the goal of fine segmentation of LV. Numerical experiments have demonstrated that the proposed method outperforms the state-of-the-art methods on the challenging CETUS benchmark in the segmentation accuracy and has a potential for practical applications.
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