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

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Marginal Space Learning,s such as local intensity and gradient. The efficiency of steerable features comes from the fact that much fewer points (defined by the sampling pattern) are needed for manipulation, in comparison to the whole volume.

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Constrained Marginal Space Learning,ion speed up to 14 times relative to the original MSL, while achieving comparable or better detection accuracy. It takes less than half a second to detect a typical 3D anatomical structure in a volume.

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Yefeng Zheng,Dorin Comaniciuulate apoptosis, including the role ofFas, FasL, and other TNF family members in apoptosis and homeostatic regulation of immune response. Recently described spl978-1-4899-0276-4978-1-4899-0274-0Series ISSN 0065-2598 Series E-ISSN 2214-8019

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Book 2014fication, 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

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Book 2014tions 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..

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Part-Based Object Detection and Segmentation,trate the robustness and accuracy of part-based object detection and segmentation on three applications, namely, left atrium segmentation in 3D C-arm CT, left ventricle detection in 2D MRI, and aorta segmentation in 3D C-arm CT.

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Optimal Mean Shape for Nonrigid Object Detection and Segmentation, more accurate initialization than the mean shapes derived through a bounding box. Experiments on aortic valve landmark detection and whole-heart segmentation demonstrate the advantages of the approach.