Cargando…
A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging
OBJECTIVES: The 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 cou...
Autores principales: | Yang, Fan, Zhang, Yan, Lei, Pinggui, Wang, Lihui, Miao, Yuehong, Xie, Hong, Zeng, Zhu |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6699314/ https://www.ncbi.nlm.nih.gov/pubmed/31467898 http://dx.doi.org/10.1155/2019/5636423 |
Ejemplares similares
-
Convolutional Neural Network for the Detection of End-Diastole and End-Systole Frames in Free-Breathing Cardiac Magnetic Resonance Imaging
por: Yang, Fan, et al.
Publicado: (2017) -
Deep learning approach for automatic segmentation of ulna and radius in dual-energy X-ray imaging
por: Yang, Fan, et al.
Publicado: (2021) -
An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance
por: Morales, Manuel A., et al.
Publicado: (2022) -
Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation
por: Chen, David, et al.
Publicado: (2022) -
Validation and quantification of left ventricular function during exercise and free breathing from real-time cardiac magnetic resonance images
por: Edlund, Jonathan, et al.
Publicado: (2022)