| 书目名称 | Marginal Space Learning for Medical Image Analysis | | 副标题 | Efficient Detection | | 编辑 | Yefeng Zheng,Dorin Comaniciu | | 视频video | | | 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 | | 图书封面 |  | | 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 | | 关键词 | 3D medical image data; Anatomical structure detection; artificial intelligence; computed tomography; hum | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4939-0600-0 | | isbn_softcover | 978-1-4939-5575-6 | | isbn_ebook | 978-1-4939-0600-0 | | copyright | Springer Science+Business Media New York 2014 |
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发表于 2025-3-21 19:05:40
