Advanced systems for comparative analysis and diagnosis.
Analysis of both brightfield and fluorescence images, enabling the direct comparison of quantitative data between various groups. Our systems support cell segmentation, spatial analysis, pixel classification, and object measurement, facilitating in-depth insightful analytical data.
In the context of cell segmentation, APS has the capability to identify the following components: positive cells, co-expression, and subcellular signals. Below, we provide a breakdown of the specifics of cell segmentation for the identification of each of these components.
Quantify the positive cell number and the percentage among the whole cell population
Quantify the subclasses of positive cells with different expression level
Quantify the expression of multiple markers in a single cell level.
Quantify the signal intensity in different subcellar compartments, such as nucleus and cytoplasm
to determine the whether the markers are co-expressed and co-localized
common parameters of the exporting data includes percentage of positive cells, percentage of co-expression in total cell population of a classified subgroup of cells in addition to the parameters mentioned in positive cell detection for each marker
Spatial analysis is used to quantify the spatial associations within a tissue microenvironment, whether its between two categorized cell types of between a single cell type and a particular molecule. In the section below, we offer examples of spatial analysis within a single study.
Pixel classification is the process of labeling individual pixels within a digital histological image. Each pixel is sorted into categories based on its attributes, which may include color, texture, or intensity. These categories correspond to specific elements, structures, or features within the studied tissue sample. This classification is commonly applied to segment and distinguish various elements in histological images, like cells, nuclei, connective tissue, or specific stains. Below, we offer an example where pixels within a tissue sample are classified as either negative or positive.
Collaborating alongside pathologists, APS scientists have the ability to use various software tools to quantify the dimensions and spatial extent of damage or particular disease regions.