Abstract: The present invention relates to systems and methods for adaptively optimizing broadband reference spectra for a multi-spectral image or adaptively optimizing reference colors for a bright-field image. The methods and systems of the present invention involve optimization techniques that are based on structures detected in an unmixed channel of the image, and involves detecting and segmenting structures from a channel, updating a reference matrix with signals estimated from the structures, subsequently unmixing the image using the updated reference matrix, and iteratively repeating the process until an optimized reference matrix is achieved.

Abstract: A facility includes systems and methods for providing a learning-based image analysis approach for the automated detection, classification, and counting of objects (e.g., cell nuclei) within digitized pathology tissue slides. The facility trains an object classifier using a plurality of reference sample slides. Subsequently, and in response to receiving a scanned image of a slide containing tissue data, the facility separates the whole slide into a background region and a tissue region using image segmentation techniques. The facility identifies dominant color regions within the tissue data and identifies seed points within those regions using, for example, a radial symmetry based approach. Based at least in part on those seed points, the facility generates a tessellation, each distinct area in the tessellation corresponding to a distinct detected object. These objects are then classified using the previously-trained classifier. The facility uses the classified objects to score slides.

Abstract: Processing of images acquired via fluorescence microscopy by identifying broadband and other undesired signals from the component signals of a scanned image, and processing selected regions of the image that are known to contain signals of interest, thereby extracting or identifying desired signals while subtracting undesired signals. One or more broadband signals are recognized by their unique signature and ubiquitous dispersion through the image. Regions of the scanned image may be tagged as consisting of predominantly broadband signals and are ignored during a spectral unmixing process. The remaining regions of the image, or selected regions of the image known to contain desired signals, may be unmixed, and the plurality of reference spectra subtracted from the components to extract or identify the target signals. The set of target signals may be refined by eliminating known or obvious sources of noise by, for instance, being compared to known or ideal sets of signals from similar materials.

Abstract: The disclosure relates to devices, systems and methods for image registration and annotation. The devices include computer software products for aligning whole slide digital images on a common grid and transferring annotations from one aligned image to another aligned image on the basis of matching tissue structure. The systems include computer-implemented systems such as work stations and networked computers for accomplishing the tissue-structure based image registration and cross-image annotation. The methods include processes for aligning digital images corresponding to adjacent tissue sections on a common grid based on tissue structure, and transferring annotations from one of the adjacent tissue images to another of the adjacent tissue images.

Abstract: Heterogeneity for biomarkers in a tissue sample can be calculated. A heterogeneity score can be combined with an immunohistochemistry combination score to provide breast cancer recurrence prognosis. Heterogeneity can be based on percent positivity determinations for a plurality of biomarkers according to how many cells in the sample stain positive. An immunohistochemistry combination score can be calculated. An imaging tool can support a digital pathologist workflow that includes designating fields of view in an image of the tissue sample. Based on the fields of view, a heterogeneity metric can be calculated and combined with an immunohistochemistry combination score to generate a breast cancer recurrence prognosis score.

Abstract: A computer-based specimen analyzer (10) is configured to detect a level of expression of genes in a cell sample by detecting dots that represent differently stained genes and chromosomes in a cell. The color of the stained genes and the chromosomes is enhanced and filtered to produce a dot mask that defines areas in the image that are genes, chromosomes, or non-genetic material. Metrics are determined for the dots and/or pixels in the image of the cell in areas corresponding to the dots. The metrics are fed to a classifier that separates genes from chromosomes. The results of the classifier are counted to estimate the expression level of genes in the tissue samples.

Abstract: Methods and systems for determining whether a particular individual is to be granted, among other things, access to information, access to a particular room or other space, or permission to perform a given act or transaction are disclosed. For example, disclosed is an exemplary method that includes performing multiple biometrics tests on the individual to retrieve multiple biometrics test results, performing at least a first data fusion operation on the multiple biometrics test results to determine an identity result, performing at least one biological state test to determine one or more biological state test results of the individual, and conditionally granting authority to the individual based on the identity result and the one or more biological state results.