Cargando…
SAUN: Stack attention U‐Net for left ventricle segmentation from cardiac cine magnetic resonance imaging
PURPOSE: Quantification of left ventricular (LV) volume, ejection fraction and myocardial mass from multi‐slice multi‐phase cine MRI requires accurate segmentation of the LV in many images. We propose a stack attention‐based convolutional neural network (CNN) approach for fully automatic segmentatio...
Autores principales: | Sun, Xiaowu, Garg, Pankaj, Plein, Sven, van der Geest, Rob J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8251605/ https://www.ncbi.nlm.nih.gov/pubmed/33544895 http://dx.doi.org/10.1002/mp.14752 |
Ejemplares similares
-
Hybrid U‐Net‐based deep learning model for volume segmentation of lung nodules in CT images
por: Wang, Yifan, et al.
Publicado: (2022) -
Fully automated segmentation of the left atrium, pulmonary veins, and left atrial appendage from magnetic resonance angiography by joint‐atlas‐optimization
por: Qiao, Menyun, et al.
Publicado: (2019) -
Temporally coherent cardiac motion tracking from cine MRI: Traditional registration method and modern CNN method
por: Qiao, Mengyun, et al.
Publicado: (2020) -
Lung cancer diagnosis using deep attention‐based multiple instance learning and radiomics
por: Chen, Junhua, et al.
Publicado: (2022) -
Development and verification of radiomics framework for computed tomography image segmentation
por: Gu, Jiabing, et al.
Publicado: (2022)