ESA-UNet for assisted diagnosis of cardiac magnetic resonance image based on the semantic segmentation of the heart.

Li, Yuanzhe; Liu, Zhiqiang; Lai, Qingquan; Li, Shuting; Guo, Yifan; Wang, Yi; Dai, Zhangsheng; Huang, Jing

Li, Yuanzhe; Liu, Zhiqiang; Lai, Qingquan; Li, Shuting; Guo, Yifan; Wang, Yi; Dai, Zhangsheng; Huang, Jing.

Afiliación

Li Y; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Liu Z; Medical Imaging Department, Guangzhou Twelfth People's Hospital, Guangzhou, China.
Lai Q; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Li S; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Guo Y; Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.
Wang Y; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Dai Z; Department of Orthopaedic Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Huang J; Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.

Front Cardiovasc Med ; 9: 1012450, 2022.

Article en En | MEDLINE | ID: mdl-36386384

Palabras clave

ESA-UNet; artificial intelligence; cardiovascular disease; conditional random field; image segmentation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline Idioma: En Revista: Front Cardiovasc Med Año: 2022 Tipo del documento: Article País de afiliación: China

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