Details

HSA cell-tracking & analysis in real-time

HSA 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.

HSA Deep Learning:
  • + 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 Excel-file

Standard cell-tracking

Standard U-Net Deep Learning


Simple intensity dependent annotation of cell borders vs original image





Standard U-Net deep learning:
  • – 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 Tumor
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.



Cell Counting

Segmentation and counting of cells.  






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.



Scratch Assay

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




Internalization

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



Blood vessel analysis

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




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.



Proliferation

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.




Intracellular Compartmentally in EM

The ultrastructural analysis of intracellular compartments using electron microscopy provides new insights into cellular Fine structures and subcellular organelle interactions.



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.