A Liver CT Atlas Segmentation Method and System Based on High Precision Registration
A high-precision, atlas technology, applied in the field of medical image processing, can solve the problem of large amount of calculation of 3D volume data
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Embodiment 1
[0075] The embodiment of the present invention discloses a liver CT atlas segmentation method based on high-precision registration. The image data used in the embodiment of the present invention comes from liver CT scans of the SLIVER07 data set and the IRCAD data set. In order to ensure that the sample size of the atlas set is sufficient when the statistical shape model is trained, the two datasets are merged here, with a total of 40 images. The size of each single-layer slice is 512x512, and the pixel size ranges from 0.56 to 0.87mm; the number of slice layers ranges from 64 to 388, and the slice thickness ranges from 0.7 to 5mm.
[0076] The present invention will be further described below in conjunction with accompanying drawings and examples. The overall automatic segmentation framework of the liver CT atlas segmentation method based on high-precision registration in the embodiment of the present invention is as follows: figure 2 As shown, the specific implementation s...
Embodiment 2
[0123] The embodiment of the present invention discloses a liver CT atlas segmentation system based on high-precision registration, including:
[0124] A model building block for building statistical shape models of all atlases;
[0125] A constraint optimization module, which is used to add the prior information of the statistical shape model to the cost function of the local registration to constrain the mutual information measure;
[0126] The segmentation processing module is used to deform the label image of the atlas to obtain multiple approximate segmentation results by using the inverse array of the deformation field obtained by registration; to perform atlas screening and label fusion on the approximate segmentation results to obtain the final segmentation result.
[0127] The content of Embodiment 1 is also applicable to this system, and will not be repeated here.
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