Image Analysis Services

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.

Brightfield & Fluorescence Analysis
  • Cellular Segmentation
  • Spatial Analysis 
  • Pixel Classification
  • Object Measuremnt

Cellular Segmentation

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.

Positive Cell Detection

Quantify the signal intensity and define the positive cells in ROIs or whole slide.

  • Quantify the positive cell number and the percentage among the whole cell population

  • Quantify the subclasses of positive cells with different expression level

  • CD-45 IHC Staining in Mouse Spleen
  • Quantification CD-45 (+) cells in Mouse Spleen
  • Cell Detection

CD-45 IHC Staining in Mouse Spleen

Quantification CD-45 (+) cells in Mouse Spleen

Cell Detection

Co-expression Detection

Quantify the expression of multiple markers in a single cell level.

  • to classify different cell types
  • to quantify the level of co-expressed and co-localized
  • Exported 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

Subcellar Signal Detection

Quantify the signal intensity in different subcellar compartments, such as nucleus and cytoplasm

  • to classify different cell types

  • 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

Co-expression of CD-8 and Ki-67
Subcellular IHC signal of CD-8 and Ki-67

Spatial Analysis

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.

Original Multiplex IHC Image

Nearest CD-68 (+) cells of each CD-20 (+) cell

Nearest PD-L1 (+) cells of each Ki-67 (+) cell

Pixel Classification

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.
Area of sample to be selected
Classification of pixels as negative or postive

Object Measurement 

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.