Abstract: Identification of regions-of-interest within cell maps is disclosed. In certain embodiments, identification of the regions-of interest is based on the use of biomarkers selected based on nucleic acid sequence data. The nucleic acid sequence data may be acquired for a homogeneous or heterogeneous set of cells present in the respective tissue sample.

Abstract: The disclosed subject matter relates to an automated determination of cell-by-cell segmentation quality of a tissue specimen sample. A training set of cells is examined by an expert to determine which cells that include “good” segmentation and which cells include “poor” segmentation. A training model is build based on the image data of the cells in the training set. Image data from cells in a test specimen is obtained and that image data is compared to the training model to determine on a cell-by-cell basis which cells in the test specimen include “good” segmentation and which cells include “poor” segmentation. The accumulated data on the cells analyzed in the test specimen can be utilized to determine an overall segmentation quality score for the area of the test specimen in which the individual cells are located in the test specimen.

Abstract: Identification of regions-of-interest within cell maps is disclosed. In certain embodiments, identification of the regions-of interest is based on the use of a plurality of biomarkers, which may be used to generate a multiplexed image of a tissue sample. Based on the identified regions, cells may be extracted and localized sequence data may be generated specific to the regions-of-interest.

Abstract: The present disclosure relates to characterization of biological samples via modular image analysis. By way of example, the analysis may include extracting plurality of regions of interest from the biological sample from a plurality of image data sets. Each region of interest may include a combination of one or more subsets of an image data set. In one example, an image data set may be generated by image segmentation. After each region of interest is extracted, at least one metric of the region of interest may be determined.

Abstract: The disclosed subject matter relates to an automated determination the stain quality and segmentation quality of a tissue sample. By way of example, separate image data is acquired of an unstained form of a biological specimen, the biological specimen stained with a nuclei marker and the biological specimen stained with a segmentation marker. A correlation map (Cr) from the separate image data and a ridgeness map (Pr) from the image data of the biological specimen stained with a segmentation marker are each determined. A staining quality score and segmentation quality score are then determined from the correlation map (Cr) and the ridgeness map (Pr).

Abstract: The disclosed subject matter relates to an automated determination of cell-by-cell segmentation quality of a tissue specimen sample. A training set of cells is examined by an expert to determine which cells that include “good” segmentation and which cells include “poor” segmentation. A training model is build based on the image data of the cells in the training set. Image data from cells in a test specimen is obtained and that image data is compared to the training model to determine on a cell-by-cell basis which cells in the test specimen include “good” segmentation and which cells include “poor” segmentation. The accumulated data on the cells analyzed in the test specimen can be utilized to determine an overall segmentation quality score for the area of the test specimen in which the individual cells are located in the test specimen.

Abstract: The present disclosure relates to characterization of biological samples. By way of example, a biological sample may be contacted with a plurality of probes specific for targets in the sample, such as probes for immune markers and segmenting probes. Acquired image data of the sample may be used to segment the images into epithelial and stromal regions to characterize individual cells in the sample based on the binding of the probes. Further, the biological sample may be characterized by a heterogeneity of the characterized cells.

Abstract: The present disclosure relates to characterization of biological samples. By way of example, a biological sample may be contacted with a plurality of probes specific for targets in the sample, such as probes for immune markers and segmenting probes. Acquired image data of the sample may be used to segment the images into epithelial and stromal regions to characterize individual cells in the sample based on the binding of the probes. Further, the biological sample may be characterized by a heterogeneity of the characterized cells.

Abstract: Dynamic linking of pathway maps and cell maps is disclosed in certain embodiments. In such embodiments, the pathway maps are linked to spatially-localized regional nucleic acid data (e.g., sequence data), as opposed to non-spatially selected nucleic acid data. The pathway map and cell map data may be linked so that interactions results in changes or updates to the linked map, such as the selection or highlighting of cells exhibiting pathway map characteristics specified by a user of updating of node values or states to correspond to that of a cell or cells selected by the user.

Abstract: Dynamic linking of pathway maps and cell maps is disclosed in certain embodiments. In such embodiments, the pathway maps are linked to spatially-localized regional nucleic acid data (e.g., sequence data), as opposed to non-spatially selected nucleic acid data. The pathway map and cell map data may be linked so that interactions results in changes or updates to the linked map, such as the selection or highlighting of cells exhibiting pathway map characteristics specified by a user of updating of node values or states to correspond to that of a cell or cells selected by the user.

Abstract: Identification of regions-of-interest within cell maps is disclosed. In certain embodiments, identification of the regions-of interest is based on the use of a plurality of biomarkers, which may be used to generate a multiplexed image of a tissue sample. Based on the identified regions, cells may be extracted and localized sequence data may be generated specific to the regions-of-interest.

Abstract: Identification of regions-of-interest within cell maps is disclosed. In certain embodiments, identification of the regions-of interest is based on the use of biomarkers selected based on nucleic acid sequence data. The nucleic acid sequence data may be acquired for a homogeneous or heterogeneous set of cells present in the respective tissue sample.

Abstract: The disclosed subject matter relates to an automated determination the stain quality and segmentation quality of a tissue sample. By way of example, separate image data is acquired of an unstained form of a biological specimen, the biological specimen stained with a nuclei marker and the biological specimen stained with a segmentation marker. A correlation map (Cr) from the separate image data and a ridgeness map (Pr) from the image data of the biological specimen stained with a segmentation marker are each determined. A staining quality score and segmentation quality score are then determined from the correlation map (Cr) and the ridgeness map (Pr).

Abstract: The present disclosure relates to characterization of biological samples via modular image analysis. By way of example, the analysis may include extracting plurality of regions of interest from the biological sample from a plurality of image data sets. Each region of interest may include a combination of one or more subsets of an image data set. In one example, an image data set may be generated by image segmentation. After each region of interest is extracted, at least one metric of the region of interest may be determined.

Abstract: Exemplary embodiments include methods, systems, and devices for enabling users to provide quality scores for indicating the quality of image analysis methods performed on images of biological tissue. An exemplary user interface displays results of an image analysis method performed on an image of biological tissue in an overlaid manner on an image of biological tissue. The exemplary user interface enable a user to provide, directly on the user interface, one or more quality scores to indicate the user's assessment of the quality of the image analysis performed on the image. Exemplary embodiments store the quality scores provided by the user in association with the image analysis method and the image of biological tissue.

Abstract: Systems and methods are disclosed for jointly presenting and analyzing morphological characteristics and biomarker expression levels of a biological sample. The systems and methods may utilize a morphological selection component to isolate a population of biological particles in a biological sample for exclusion from further processing. In addition, the systems and methods may simultaneously render morphological and statistical representations of the biological sample on a user interface.

Abstract: The invention relates generally to a method for interactive viewing and analysis of high content data in a biological pathway context. The high content data maybe related to the expression of biomarkers within a tissue, cellular, or cellular compartment of individual cell such that the data may reveal patterns of expression to identify a biological process, a clinical diagnosis or prognosis.

Abstract: The invention relates generally to a process of analyzing and visualizing the expression of biomarkers in an individual cell wherein the cell is examined to develop patterns of expression by using a grouping algorithm, and a system to perform and display the analysis.

Abstract: Embodiments disclosed herein include methods, systems, and devices for determining a positive or negative correlation between a clinical outcome and one or more features of biological tissue. An exemplary user interface enables a user to select a clinical outcome and one or more aspects of a displayed field-of-view of biological tissue. Exemplary embodiments may automatically perform correlation analysis between the selected clinical outcome and one or more features characteristic of the user-selected aspects of the field-of-view of biological tissue. For example, exemplary embodiments may automatically perform correlation analysis between the selected clinical outcome and one or more features characteristic of one or more biological units in the biological tissue.

Abstract: The quantitative evaluation of biomarker-probe activity is disclosed. In certain embodiments, the biomarker-probe activity may be quantified and analyzed using biodistributions generated using a model. In some embodiments, such biodistributions may be used to generate simulated images from which quantitative thresholds may be derived. In some embodiments, the quantitative thresholds may be used to analyze the biodistributions.