Geodesic patch-based segmentation

被引:0
|
作者
Wang, Zehan [1 ]
Bhatia, Kanwal K. [1 ]
Glocker, Ben [1 ]
Marvao, Antonio [2 ]
Dawes, Tim [2 ]
Misawa, Kazunari [3 ]
Mori, Kensaku [4 ,5 ]
Rueckert, Daniel [1 ]
机构
[1] Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
[2] Institute of Clinical Sciences, Imperial College London, London, United Kingdom
[3] Aichi Cancer Center, Nagoya, Japan
[4] Department of Media Science, Nagoya University, Nagoya, Japan
[5] Information and Communications Headquarters, Nagoya University, Nagoya, Japan
关键词
Magnetic resonance imaging - Medical imaging - Computerized tomography - Image segmentation - Medical computing;
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学科分类号
摘要
Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. Too small, and it does not account for enough registration error; too big, and it becomes more likely to select incorrect patches of similar appearance for label fusion. This paper presents a novel patch-based label propagation approach which uses relative geodesic distances to define patient-specific coordinate systems as spatial context to overcome this problem. The approach is evaluated on multi-organ segmentation of 20 cardiac MR images and 100 abdominal CT images, demonstrating competitive results. © Springer International Publishing Switzerland 2014.
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页码:666 / 673
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