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Marginal Space Learning for Medical Image Analysis
Angielski
154 b
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30 dni na zwrot towaru
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Automatic detection and segmentation of anatomical structures in 3D medical image data are prerequisites for subsequent image measurements and disease quantification, and therefore have multiple applications in medical imaging. In this book, we present an efficient object detection and segmentation method, called Marginal Space Learning, which runs at a sub-second speed on a current desktop computer, much faster than the state-of-the-art. Trained with a sufficient number of data sets, the method is also robust under imaging artifacts and noise. The book showcases over 30 applications of Marginal Space Learning and its extensions on segmenting various anatomical structures (e.g., the heart and liver) in all major medical imaging modalities (CT, MRI, and ultrasound), demonstrating its efficiency and robustness.
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