Sparse Appearance Learning-based Segmentation
First Claim
Patent Images
1. A method for segmentation of a vessel, the method comprising:
- extracting a centerline for a vessel represented in medical imaging data representing a patient, the extracting being a function of a cost term;
calculating the cost term as a function of similarity of patches of the medical imaging data to machine-learned appearance patterns at multiple scales relative to the medical imaging data, the cost term indicating membership as in the vessel or not in the vessel of different locations for the patches;
segmenting the vessel as represented in the medical imaging data, the segmenting being a function of the centerline; and
generating an image of the vessel segmented from the medical imaging data.
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Abstract
The coronary sinus or other vessel is segmented by finding a centerline and then using the centerline to locate the boundary of the vessel. For finding the centerline, a refinement process uses multi-scale sparse appearance learning. For locating the boundary, the lumen is segmented as a graph cut problem.
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Citations
20 Claims
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1. A method for segmentation of a vessel, the method comprising:
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extracting a centerline for a vessel represented in medical imaging data representing a patient, the extracting being a function of a cost term; calculating the cost term as a function of similarity of patches of the medical imaging data to machine-learned appearance patterns at multiple scales relative to the medical imaging data, the cost term indicating membership as in the vessel or not in the vessel of different locations for the patches; segmenting the vessel as represented in the medical imaging data, the segmenting being a function of the centerline; and generating an image of the vessel segmented from the medical imaging data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. In a non-transitory computer readable storage medium having stored therein data representing instructions executable by a programmed processor for segmentation of a vessel, the storage medium comprising instructions for:
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determining probabilities of locations in a volume of a patient represented by medical imaging data being at a vessel boundary; constructing a Markov random field graph from the probabilities; extracting a contour of the vessel boundary from the Markov random field graph; and segmenting the vessel with the vessel boundary. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A method for segmentation of a coronary sinus vessel, the method comprising:
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extracting a centerline of the coronary sinus vessel in a volume represented by computed tomography data, the extracting being a function of sparse appearance modeling at multiple scales; identifying voxels of the volume belonging to the coronary sinus vessel as a function of the centerline and a graph optimization; and generating an image from the voxels of the volume identified as belonging to the coronary sinus vessel.
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Specification