Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound

The automated and accurate carotid plaque segmentation in B-mode ultrasound (US) is an essential part of stroke risk stratification. Previous segmented methods used AtheroEdge™ 2.0 (AtheroPoint™, Roseville, CA) for the common carotid artery (CCA). This study focuses on automated plaque segmentation...

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Veröffentlicht in:	Computers in biology and medicine 2021-09, Vol.136, p.104721-104721, Article 104721
Hauptverfasser:	Jain, Pankaj K., Sharma, Neeraj, Giannopoulos, Argiris A., Saba, Luca, Nicolaides, Andrew, Suri, Jasjit S.
Format:	Artikel
Sprache:	eng
Schlagworte:	Area Arteriosclerosis Artificial intelligence Atherosclerosis Automation Cancer Cardiovascular disease Carotid arteries Carotid artery Cross-entropy Deep learning Dice similarity coefficient Entropy Health risks Hybrid deep learning Internal carotid artery Magnetic resonance imaging On-line systems Performance evaluation Plaque segmentation Risk groups Segmentation Stroke Ultrasonic imaging Ultrasound
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