Software

zenCELL owl Software

The Software integrated image processing algorithms support the continuous long-term monitoring and provide fast and accurate information about the current state of the sample to be observed. The real-time data analysis provides information about the current cell count and the degree of coverage of the substrate surface of the image section with cells. The total number of cells as well as the number of cells attached to the substrate and the number of cells detached from substrate are evaluated. Start your project and capture the images of your cell culture in your individual frequency. Create a time-lapse video or export a dataset for your scientific paper.

Overview - Well View

The overview window shows an overview of all available cameras.

>Your groupings are marked with a colored frame.

Open the Well View window by a simple click in the desired well.

The Well View window provides a detailed view of the selected well.

Make settings as well as image exports for the respective well.

Time-lapse video

The Well History window is the summary of the experiment at the current time.

Watch your time-lapse video.

You can view different diagrams. Choose between coverage, cell count, adherent cells and detached cells.

Export your measurement data in a .csv file and/or save the selected graph or image as an image file.

 

 

Custom Plot

The Custom Plot is an overview for the comparison of the different recorded measurement curves.

Select the desired measurement curves in grouping and display the mean value of the measurement curves. This allows a simple comparison of different measurement series in one test.

Take a look at the corresponding standard deviation.

You can view different diagrams. Choose between coverage, cell count, adherent cells and detached cells.

Save your custom plot as an image file and/or export it as a .csv file.

 

 

 

Fig. 1

 

 

 

Fig. 2

Coverage algorithm

The algorithm for coverage is defined by a percentage calculation of the "overgrown" area. This area is defined by the empty background and contrast via cells. The covered areas are marked green in figure 2. It is specified that no coverage higher than 100% is defined, since the overlapping of cells is not identified by the algorithm.

  • It should be noted that small gaps between cells and areas beyond the cell membranes are partly calculated in addition to the covered area.

Coverage algorithm

The cell counting algorithm was initiated by deep learning. Defined cells are coloured in the figure 4 in red or blue. The size and shape of the cell is decisive for classifying them as adherent or detached cells.

  • Please note that as soon as the cells overlap and the field view has reached 100%, the cells are only counted from the bottom focal plane in a 2D visualization. The cell layers above can no longer be counted. The setting of the focal plane by the autofocus or manually is decisive for the algorithm.

 

 

 

Fig. 3

 

 

 

Fig. 4