发明名称 |
Learning-based aorta segmentation using an adaptive detach and merge algorithm |
摘要 |
Systems and methods for segmenting a structure of interest in medical imaging data include generating a binary mask highlighting structures in medical imaging data, the highlighted structures comprising a connected component including a structure of interest. A probability map is computed by classifying voxels in the highlighted structures using a trained classifier. A plurality of detaching operations is performed on the highlighted structures to split the connected component into a plurality of detached connected components. An optimal detaching parameter is determined representing a number of the detaching operations. A detached connected component resulting from performing the number of detaching operations corresponding to the optimal detaching parameter is classified as the structure of interest based on the probability map and the trained classifier. |
申请公布号 |
US9589211(B2) |
申请公布日期 |
2017.03.07 |
申请号 |
US201514707503 |
申请日期 |
2015.05.08 |
申请人 |
Siemens Healthcare GmbH |
发明人 |
Lay Nathan;Liu David;Kretschmer Jan;Zhou Shaohua Kevin |
分类号 |
G06K9/00;G06K9/62;G06T7/00;G06K9/52;G06T11/20;G06T19/20;G06T15/08 |
主分类号 |
G06K9/00 |
代理机构 |
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代理人 |
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主权项 |
1. A method for segmenting a structure of interest in medical imaging data, comprising:
generating a binary mask highlighting structures in medical imaging data, the highlighted structures comprising a connected component including a structure of interest; computing a probability map by classifying voxels in the highlighted structures using a trained classifier; performing a plurality of detaching operations on the highlighted structures to split the connected component into a plurality of detached connected components; determining an optimal detaching parameter representing a number of the detaching operations; and classifying a detached connected component resulting from performing the number of detaching operations corresponding to the optimal detaching parameter as the structure of interest based on the probability map and the trained classifier. |
地址 |
Erlanger DE |