Method and apparatus for tissue modeling
First Claim
1. A method for tissue modeling using at least one tissue image derived from biological tissue, said at least one tissue image having cells therein, said method comprising for each tissue image:
- clustering data derived from the tissue image to generate cluster vectors such that each cluster vector represents a portion of the tissue image;
generating cell information, comprising assigning a cell class or a background class to each of the cluster vectors; and
generating a cell-graph for the tissue image from the generated cell information, said generating the cell-graph comprising generating nodes and edges of the cell-graph, said edges connecting at least two of the nodes together, each node representing at least one cell of the biological tissue or a portion of a single cell of the biological tissue.
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Abstract
A method and apparatus for tissue modeling using at least one tissue image having cells therein and derived from biological tissue. Data derived from the tissue image is clustered to generate cluster vectors such that each cluster vector represents a portion of the tissue image. Cell information is generated which assigns a cell class or a background class to each of the cluster vectors. A cell-graph is generated for the tissue image from the generated cell information. The generated cell-graph comprises nodes and edges. The edges connect at least two of the nodes together. Each node represents at least one cell of the biological tissue or a portion of a single cell of the biological tissue. At least one metric may be computed from the nodes and edges, and the biological tissue may be classified based on the at least one metric.
34 Citations
53 Claims
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1. A method for tissue modeling using at least one tissue image derived from biological tissue, said at least one tissue image having cells therein, said method comprising for each tissue image:
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clustering data derived from the tissue image to generate cluster vectors such that each cluster vector represents a portion of the tissue image;
generating cell information, comprising assigning a cell class or a background class to each of the cluster vectors; and
generating a cell-graph for the tissue image from the generated cell information, said generating the cell-graph comprising generating nodes and edges of the cell-graph, said edges connecting at least two of the nodes together, each node representing at least one cell of the biological tissue or a portion of a single cell of the biological tissue. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34)
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35. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, said computer readable program code comprising an algorithm adapted to implement a method for tissue modeling using at least one tissue image derived from biological tissue, said at least one tissue image having cells therein, clustering data having been derived from the tissue image to generate cluster vectors such that each cluster vector represents a portion of the tissue image, cell information having been generated by assignment of a cell class or a background class to each of the cluster vectors, said method comprising:
generating a cell-graph for the tissue image from the generated cell information, said generating the cell-graph comprising generating nodes and edges of the cell-graph, said edges connecting at least two of the nodes together, each node representing at least one cell of the biological tissue or a portion of a single cell of the biological tissue. - View Dependent Claims (36, 37, 38, 39, 40, 41, 42, 43, 44, 45)
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46. An apparatus for tissue modeling using at least one tissue image derived from biological tissue, said at least one tissue image having cells therein, said apparatus comprising for each tissue image:
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means for clustering data derived from the tissue image to generate cluster vectors such that each cluster vector represents a portion of the tissue image;
means for generating cell information, comprising assigning a cell class or a background class to each of the cluster vectors; and
means for generating a cell-graph for the tissue image from the generated cell information, said means for generating the cell-graph comprising means for generating nodes and edges of the cell-graph, said edges connecting at least two of the nodes together, each node representing at least one cell of the biological tissue or a portion of a single cell of the biological tissue. - View Dependent Claims (47, 48, 49, 50, 51, 52, 53)
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Specification