Authors:
- Presents an award winning image analysis technology (Thomas Edison Patent Award, MICCAI Young Investigator Award) that achieves object detection and segmentation with state-of-the-art accuracy and efficiency
- Flexible, machine learning-based framework, applicable across multiple anatomical structures and imaging modalities
- Thirty five clinical applications on detecting and segmenting anatomical structures such as heart chambers and valves, blood vessels, liver, kidney, prostate, lymph nodes, and sub-cortical brain structures, in CT, MRI, X-Ray and Ultrasound.
- Includes supplementary material: sn.pub/extras
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Table of contents (9 chapters)
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Front Matter
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Back Matter
About this book
Automatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications. This book presents an efficient object detection and segmentation framework, called Marginal Space Learning, which runs at a sub-second speed on a current desktop computer, faster than the state-of-the-art. Trained with a sufficient number of data sets, Marginal Space Learning is also robust under imaging artifacts, noise and anatomical variations. The book showcases 35 clinical applications of Marginal Space Learning and its extensions to detecting and segmenting various anatomical structures, such as the heart, liver, lymph nodes and prostate in major medical imaging modalities (CT, MRI, X-Ray and Ultrasound), demonstrating its efficiency and robustness.
Keywords
- 3D medical image data
- Anatomical structure detection
- artificial intelligence
- computed tomography
- human body parsing
- human organ pose estimation
- intelligent image analysis system
- machine learning
- magnetic resonance imaging
- marginal space learning
- medical image analysis
- medical image segmentation
- medical imaging
- object detection
- organ segmentation
- ultrasound
Reviews
Authors and Affiliations
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Imaging and Computer Vision, Siemens Corporate Technology, Princeton, USA
Yefeng Zheng, Dorin Comaniciu
Bibliographic Information
Book Title: Marginal Space Learning for Medical Image Analysis
Book Subtitle: Efficient Detection and Segmentation of Anatomical Structures
Authors: Yefeng Zheng, Dorin Comaniciu
DOI: https://doi.org/10.1007/978-1-4939-0600-0
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Science+Business Media New York 2014
Hardcover ISBN: 978-1-4939-0599-7
Softcover ISBN: 978-1-4939-5575-6
eBook ISBN: 978-1-4939-0600-0
Edition Number: 1
Number of Pages: XX, 268
Number of Illustrations: 64 b/w illustrations, 58 illustrations in colour
Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Imaging / Radiology, Artificial Intelligence