GROUP SPARSITY MODEL FOR IMAGE UNMIXING

摘要

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.

主权项

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.