Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on m...

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Vydané v:	IEEE transactions on medical imaging Ročník 35; číslo 5; s. 1217 - 1228
Hlavní autori:	Ghesu, Florin C., Krubasik, Edward, Georgescu, Bogdan, Singh, Vivek, Yefeng Zheng, Hornegger, Joachim, Comaniciu, Dorin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.05.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Aortic Valve - diagnostic imaging Context Databases, Factual Deep learning Echocardiography, Transesophageal Feature extraction Humans image parsing Image Processing, Computer-Assisted - methods Image segmentation Learning Machine Learning marginal space learning Mathematical models Medical imaging Neural Networks (Computer) Parametrization Patients Pattern Recognition, Automated - methods Robustness Segmentation Shape sparse representations Three dimensional three-dimensional (3D) object detection and segmentation Three-dimensional displays Volumetric analysis
ISSN:	0278-0062, 1558-254X, 1558-254X
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