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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Bibliographic Details
Published in:	IEEE transactions on medical imaging Vol. 35; no. 5; pp. 1217 - 1228
Main Authors:	Ghesu, Florin C., Krubasik, Edward, Georgescu, Bogdan, Singh, Vivek, Yefeng Zheng, Hornegger, Joachim, Comaniciu, Dorin
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.05.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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