Cargando…
Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning
OBJECTIVE: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. MATERIALS AND METHODS: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 trainin...
Autores principales: | Koo, Hyun Jung, Lee, June-Goo, Ko, Ji Yeon, Lee, Gaeun, Kang, Joon-Won, Kim, Young-Hak, Yang, Dong Hyun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Society of Radiology
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7231613/ https://www.ncbi.nlm.nih.gov/pubmed/32410405 http://dx.doi.org/10.3348/kjr.2019.0378 |
Ejemplares similares
-
Computed Tomography-Based Ventricular Volumes and Morphometric Parameters for Deciding the Treatment Strategy in Children with a Hypoplastic Left Ventricle: Preliminary Results
por: Goo, Hyun Woo, et al.
Publicado: (2018) -
Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts
por: Lee, June-Goo, et al.
Publicado: (2021) -
Late Gadolinium Enhancement of Left Ventricular Papillary Muscles in Patients with Mitral Regurgitation
por: Lim, Su Jin, et al.
Publicado: (2021) -
Assessment of Left Ventricular Myocardial Diseases with Cardiac Computed Tomography
por: Ko, Sung Min, et al.
Publicado: (2019) -
Right Ventricular Mass Quantification Using Cardiac CT and a Semiautomatic Three-Dimensional Hybrid Segmentation Approach: A Pilot Study
por: Goo, Hyun Woo
Publicado: (2021)