GROUP SPARSITY MODEL FOR IMAGE UNMIXING
First Claim
1. A method for spectral unmixing of an image obtained from a biological tissue sample being stained by multiple stains using a group Lasso criterion, the method comprising:
- inputting image data obtained from the biological tissue sample;
reading reference data from a memory, the reference data being descriptive of the stain color of each one of the multiple stains;
reading colocation data from the memory, the colocation data being descriptive of groups of the stains, each group comprising stains that can be collocated in the biological tissue sample, and each group forming a group for the group Lasso criterion, at least one of the groups having a size of two or above;
calculating a solution of the group Lasso criterion for obtaining the unmixed image using the reference data as a reference matrix.
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Abstract
Systems and methods described herein relate, among other things, to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be leveraged to explicitly model the fractions of stain contributions from the co-localized biomarkers into one group to yield a least squares solution within the group. A sparse solution may be obtained among the groups to ensure that only a small number of groups with a total number of stains being less than the upper bound are activated.
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Citations
20 Claims
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1. A method for spectral unmixing of an image obtained from a biological tissue sample being stained by multiple stains using a group Lasso criterion, the method comprising:
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inputting image data obtained from the biological tissue sample; reading reference data from a memory, the reference data being descriptive of the stain color of each one of the multiple stains; reading colocation data from the memory, the colocation data being descriptive of groups of the stains, each group comprising stains that can be collocated in the biological tissue sample, and each group forming a group for the group Lasso criterion, at least one of the groups having a size of two or above; calculating a solution of the group Lasso criterion for obtaining the unmixed image using the reference data as a reference matrix. - View Dependent Claims (2, 3)
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4. A tissue analysis system comprising:
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a color data storage module to store, for each of a plurality of markers, color data indicative of a color of tissue marked by the respective marker; a co-location data storage module to store co-location data defining a plurality of groups of said markers, each group consisting of markers having an affinity to a respective common tissue feature; a tissue image data storage module to store a plurality of pixels representative of a tissue image, each pixel comprising color information; and a tissue image analysis module to unmix said tissue image, wherein said tissue image analysis module is configured to read said color data from said color data storage module, said co-location data from said co-location data storage module and said pixels from said tissue image data storage module, and to calculate, for each of said pixels and for each of said groups, a linear combination of the colors of the markers of the respective group that yields a minimum difference between said color information of the respective pixel and said linear combination of colors, and wherein, for each of said pixels, said tissue image analysis module is to determine a group for which said minimum difference is smallest and outputs said tissue feature of said group as an analysis result. - View Dependent Claims (5, 6, 7, 8)
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9. A tissue analysis method, comprising:
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storing, for each of a plurality of markers, color data indicative of a color of tissue marked by the respective marker; storing co-location data defining a plurality of groups of said markers, each group consisting of markers having an affinity to a respective common tissue feature; storing a plurality of pixels representative of a tissue image, each pixel comprising color information; unmixing said tissue image using said color data, said co-location data and said pixels by calculating, for each of said pixels and for each of said groups, a linear combination of the colors of the markers of the respective group that yields a minimum difference between said color information of the respective pixel and said linear combination of colors; determining, for each of said pixels, a group for which said minimum difference is smallest; and outputting said tissue feature of said group as an analysis result. - View Dependent Claims (10, 11, 12, 13)
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14. A non-transitory digital storage medium storing instructions executable by a processor to perform operations comprising:
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generating a group sparsity model wherein a fraction of a stain contribution from colocation markers is assigned within a single group and a fraction of a stain contribution from non-colocation markers is assigned within separate groups; and solving the group sparsity model using an unmixing algorithm to yield a least squares solution within each group. - View Dependent Claims (15, 16)
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17. A system for unmixing an image, comprising:
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a processor; and a memory coupled to the processor, the memory to store digitally encoded instructions that, when executed by the processor, cause the processor to perform operations comprising; generating a group sparsity framework using known co-location information of a plurality of biomarkers within an image of a tissue section;
wherein a fraction of each stain contribution is assigned to a different group based on the known co-location information; andsolving the group sparsity model using an unmixing algorithm to yield a least squares solution for each group. - View Dependent Claims (18, 19)
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20. A non-transitory storage medium storing instructions executable by a processor to cause the processor to perform operations comprising:
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modeling an RGB image unmixing problem using a group sparsity framework, in which fractions of stain contributions from a plurality of colocation markers are modeled within a same group and fractions of stain contributions from a plurality of non-colocation markers are modeled in different groups; providing co-localization information of the plurality of colocation markers to the modeled group sparsity framework; solving the modeled framework using a group lasso to yield a least squares solution within each group, wherein the least squares solution corresponds to the unmixing of the colocation markers; and yielding a sparse solution among the groups that corresponds to the unmixing of the non-colocation markers.
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Specification