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Titlebook: Marginal Space Learning for Medical Image Analysis; Efficient Detection Yefeng Zheng,Dorin Comaniciu Book 2014 Springer Science+Business M

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書目名稱Marginal Space Learning for Medical Image Analysis
副標(biāo)題Efficient Detection
編輯Yefeng Zheng,Dorin Comaniciu
視頻videohttp://file.papertrans.cn/624/623875/623875.mp4
概述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 eff
圖書封面Titlebook: Marginal Space Learning for Medical Image Analysis; Efficient Detection  Yefeng Zheng,Dorin Comaniciu Book 2014 Springer Science+Business M
描述.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..
出版日期Book 2014
關(guān)鍵詞3D medical image data; Anatomical structure detection; artificial intelligence; computed tomography; hum
版次1
doihttps://doi.org/10.1007/978-1-4939-0600-0
isbn_softcover978-1-4939-5575-6
isbn_ebook978-1-4939-0600-0
copyrightSpringer Science+Business Media New York 2014
The information of publication is updating

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Yefeng Zheng,Dorin Comaniciuecific leading to the prevailing notion that most of the T cell expansion represents cytokine-mediated bystander activation and/or cross reactive stimulation of non specific cells. To re-examine this issue we quantitated antigen specific CD8 T cells during acute LCMV infection of mice using three se
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Yefeng Zheng,Dorin Comaniciu them off. In addition, fine-tuning can be achieved through signals that amplify or downmodulate responses. The complexity in signal transduction pathways that regulate these responses is daunting. Even terminal responses, such as degranulation by effector cells in the immune system, do not follow a
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