Cell tracking and analysis in real time

HSA in-house developed deep learning

Tracking of cellular morphology changes and behavior in concert with surrounding cells. Cells with increasing area are marked blue, decreasing area red. Length of individual cell borders, circumference, area and percentile change, compared to previous frame (Δ area, Δ circum.), are tracked in real time.

Self developed AI networks:
  • + Cell border gaps in the original image are closed intelligently
  • + Automatic tracking of length of individual cell borders, cellular morphology changes and behavior in concert with surrounding cells
  • + Visual representation of selected morphology changes (Area, circularity, neighbor cell behavior etc.)
  • + Simple export of all statistical data into an Exel file


Standard Deep Learning U-Net

Simple intensity dependent annotation of cell borders vs original image

Old U-Net based AI network:
  • - Gaps in the original image are not closed, therefore subsequent statistical analysis is impaired
  • - Tracking of cellular parameters difficult or only possible by additional programs
  • - No visual representation of selected changes in cellular morphology

Counting and segmentation of
cells in chamber slides

Detection and segmentation of nuclei and cytoplasm in cultured cells in chamber slides.

Segmentation of Tumour
and Stroma cells

Segmentation of nuclei and cytoplasm in tumour cells and cytoplasmic regions in stroma cells. If nuclear staining is not present or unspecific, nuclei can be detected by nuclear “holes” in cytoplasmic staining.

Kidney Tool

Segmentation of glomeruli and tubuli by using a Deep Learning model for brightfield data.

Kidney Tool

Segmentation of glomeruli and their compartments by using a tensorflow model for brightfield data.

Intracellular Compartments in EM

Ultra-structural analysis of intracellular compartments using electron microscopy provides new insights into cellular fine structures and sub-cellular organelle interactions.

Endoplasmic Reticulum

The endoplasmic reticulum (ER) is a dynamic structure consisting of branched domains and tubular sections. Automatic segmentation opens up new possibilities for quantitative analysis and efficient representation of this membrane structure at the ultra-structural level.

Cell Counting

Segmentation and counting of cells.  


Analysis of insulin receptors inside and outside of cytoplasm and the change over time.

Scratch Assay

Analysis of samples with scratches and the change of cell area over time.


Real time detection on microscopy devices

You look through the microscope and see in real time markings of the objects relevant to you. Subsequently, an automatic quantification of the detected objects takes place.

Blood vessel analysis

Blood vessels can be marked with immunohistochemistry. This allows blood vessel counting and analysis of blood vessel distribution.  


Segmentation of cells over time to measure proliferation. The analysis is done on Label Free Data.

Colorectal cancer

Segmentation of brightfield data with colorectal cancer using a deep learning model.

Lung analysis

Automated classification of tumors in non-small cell lung cancer (NSCLC) and quantification of tumor/stroma.

Annotation Software to set region of interests (ROI)

Manual drawing of regions of interest (ROI) as well as automatic ROI detection. Image analysis can be restricted to ROIs or different analysis methods can be allocated to different ROI types within one image.

Screening with oncology markers

Tissue evaluation with cancer specific markers, for example PD-L1, Ki67, p21, p53, Stathmin, bTubulin, etc.

TMA Screening & Analysis

Analysis Software for Tissue MicroArrays (TMA) with variable number of cores. Semi automatic naming of TMA cores. Export of TMA core results together with cores names into excel files.