Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data

We present a new statistical pattern recognition approach for the problem of left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the current time step is estimated based on the appear...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 35; číslo 11; s. 2592 - 2607
Hlavní autori:	Carneiro, G., Nascimento, J. C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Los Alamitos, CA IEEE 01.11.2013 IEEE Computer Society
Predmet:	Algorithms Applied sciences Artificial Intelligence Biological and medical sciences Cardiovascular system Computational modeling Computer science; control theory; systems Computer Simulation Connectionism. Neural networks deep belief networks discriminative classifiers dynamical model Echocardiography - methods Endocardium - diagnostic imaging Exact sciences and technology Heart Ventricles - diagnostic imaging Humans Image Interpretation, Computer-Assisted - methods Image segmentation Imaging Investigative techniques, diagnostic techniques (general aspects) Learning and adaptive systems Left ventricle segmentation Medical sciences Models, Cardiovascular Motion segmentation particle filters Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Shape Tracking Training Ultrasonic investigative techniques Ventricular Dysfunction, Left - diagnostic imaging Great depth Left ventricle segmentation Tracking Image processing Ultrasound imaging Endocardium discriminative classifiers Modeling Belief networks Sequential method Classification Cardiology Dynamic model Ultrasound Particle filter Importance sampling Multimodel Statistical analysis Probabilistic approach Healthy subject Pattern recognition Neural network Experimental study dynamical model Left ventricle Image segmentation deep belief networks Remolded sample particle filters Tracking(movable target) Time complexity Edge detection Artificial intelligence
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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