ArticlePhysics in medicine and biology2013
Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set.
Article in Physics in medicine and biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Evaluation of state-of-the-art deep learning models in the segmentation of the left and right ventricles in parasternal short-axis echocardiograms.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Leukocyte Telomere Length and Cardiac Structure and Function: A Mendelian Randomization Study.Journal of the American Heart Association · 2024Article
- Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis.Cardiovascular ultrasound · 2021Review
- Automatic Segmentation of Left Ventricle in Echocardiography Based on YOLOv3 Model to Achieve Constraint and Positioning.Computational and mathematical methods in medicine · 2021Article
- Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks.Journal of the Royal Society, Interface · 2020Article
- Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Ultrasound Segmentation of Rat Hearts Using Convolution Neural Networks.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Estimating cardiac fiber orientations in pig hearts using registered ultrasound and MR image volumes.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- Simulating cardiac ultrasound image based on MR diffusion tensor imaging.Medical physics · 2015Article
- DTI template-based estimation of cardiac fiber orientations from 3D ultrasound.Medical physics · 2015Article
- Register cardiac fiber orientations from 3D DTI volume to 2D ultrasound image of rat hearts.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- Quantitative Wavelength Analysis and Image Classification for Intraoperative Cancer Diagnosis with Hyperspectral Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- Measuring myofiber orientations from high-frequency ultrasound images using multiscale decompositions.Physics in medicine and biology · 2014Article
- Spectral-Spatial Classification Using Tensor Modeling for Cancer Detection with Hyperspectral Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Mapping Cardiac Fiber Orientations from High-Resolution DTI to High-Frequency 3D Ultrasound.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging.Journal of biomedical optics · 2014Article
- 3DProceedings of SPIE--the International Society for Optical EngineeringArticle
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3 authors.
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Abstract
An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 ± 1.7% and 87.3 ± 1.9%, the absolute distances were 2.0 ± 0.42 mm and 1.79 ± 0.45 mm, and the Hausdorff distances were 6.86 ± 1.71 mm and 7.02 ± 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.
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