File Types

Bio Applications can only be applied to suitable image files. If you try to use them with an unsuitable image type, a message is displayed. The following types of images are not supported for this application:

  1. Z-Stacks
  2. Unprocessed Airyscan data
  3. Unprocessed Apotome data
  4. Multi-phase images
  5. Multi-block images
  6. PSF images
  1. You have opened an image that is typical for the analysis scenario.
  2. You have set up a general setting for your application, see Creating a General Bio Application Setting.
  1. On the Analysis tab, in the Bio Applications tool, click Automated Spot Detection.
  2. The parameters are displayed.
  3. Select your created setting as well as your image and click Create Setting.
  4. The Bio Applications wizard opens.
  5. Enter a Name for the nuclei you want to segment, select the Channel which contains the necessary information as well as a Color for the resulting masks.
  6. For the segmentation of the nuclei, select Manual, if you want to define the objects manually by clicking in the image. Otherwise, select Automatic. In the Manual mode, you can click on the objects you want to segment and the lower and higher threshold values will be adapted automatically. Alternatively you can directly enter a Threshold (lowest and highest value) or use the Histogram for the pixel values used by the segmentation. For color images, you can set the threshold for each color channel.
  7. If you want to use machine learning for segmentation, click Semantic or Instance for semantic or instance segmentation. The prerequisite for semantic segmentation is that you have installed the 3rd party Python Tools during the installation of ZEN. For instance segmentation, you need to have the Docker Desktop software running.
  8. A dropdown to select your model is displayed. It contains your own trained and imported networks if they have been trained on one channel. For semantic segmentation, a default neural network for the segmentation of fluorescently labeled nuclei is provided.
  9. In the AI Model dropdown, select the model you want to use for segmentation.
  10. Select the Model Class that should be used for segmentation and set a Min. Confidence. For instance segmentation you also have to select the AI Model Version.
  11. If you want to perform a rolling ball background subtraction, click On.
  12. Set the lowest and highest value for the Area and Circularity measurement to filter out unwanted objects.
  13. A result preview is displayed by the Image View. The unwanted objects are displayed in white.
  14. If you manually want to include certain objects, for Pick to Include click + and the click on the object in the image.
  15. The values employed for the filters Area and Circularity are updated accordingly to include the selected object and any other objects that fulfil the newly adapted filter criteria.
  16. Set the distance between the boundary of the masks of the segmented nuclei and the rings where the spots should be detected. A negative value means that the ring already begins inside of the nucleus. A positive value creates a ring that starts in a distance from the boundary of the nuclei masks.
  17. Set the width of the rings displayed in the image.
  18. Click Next.
  19. The second step of the wizard opens.
  20. Enter a Name for the spots, select the Channel with which the spots are stained as well as a Color for the resulting masks.
  21. For the segmentation of the nuclei, select Manual, if you want to define the objects manually by clicking in the image. Otherwise, select Automatic. In the Manual mode, you can click on the objects you want to segment and the lower and higher threshold values will be adapted automatically. Alternatively you can directly enter a Threshold (Low and High) or use the Histogram for the pixel values used by the segmentation. For color images, you can set the threshold for each color channel.
  22. If you want to use machine learning for segmentation, click Semantic or Instance for semantic or instance segmentation.
  23. A dropdown to select your model is displayed.
  24. In the AI Model dropdown, select the model you want to use for segmentation.
  25. Select the Model Class that should be used for segmentation and set a Min. Confidence. For instance segmentation you also have to select the AI Model Version.
  26. A result preview is displayed by the Image View.
  27. For the Spots step, a rolling ball background subtraction is automatically enabled.
  28. If you do not want to perform a rolling ball background subtraction, click Off.
  29. Set the lowest and highest value for the Area and Circularity measurement to filter out unwanted objects.
  30. A result preview is displayed by the Image View. The unwanted objects are displayed in white.
  31. If you manually want to include certain objects, for Pick to Include click + and then click on the object in the image.
  32. The values employed for the filters Area and Circularity are updated accordingly to include the selected object and any other objects that fulfil the newly adapted filter criteria.
  33. Click Finish.
  1. You have created a setting for Spot Detection which can now be used to analyze images by clicking Run Analysis, see Running Bio Applications.