![]() With Select (indicated in green above) you can define a region of interest (ROI) ( x, y, width, height) for the currently selected image.įor a long time series, this task can be very tedious. Side, which are explained in more detail below. A number of options then appears of the right ![]() To open the cropping settings, select the 2.1 Cropping tab in the 2 Segmentation tab. Transfer segmentation from one channel to another The following steps are organized according to the workflow for a general segmentation process. In case you already have a single-cell segmentation and want to import it into BiofilmQ, please follow the steps in Import of single-cell segmentation. Segmentation for Case 2 (import of single-cell segmentation): ¶ We will explain the influences of each parameter in the following step-by-step explanation. Segmentation results by optimizing the segmentation parameters according to your input files. Even though the default values will often produce reasonable results, you can improve the Once the segmentation is finished, you can inspect the segmentation result by using the Overlay-button in the Image preview-panel. Press button Segment cells to start the segmentation. Use the Selected image(s)-button in the Image range-panel to include the selection in the segmentation.ĭirectly jump to 2.6 Object declumping and add a grid side length. Select one or several image file(s) in the Files-panel. Alternatively, users may import a single-cell segmentation via a binary image or labeled image into BiofilmQ, to perform single cell cytometry with BiofilmQ. ![]() For these cubes, spatially resolved properties can be computed in a “cube cytometry”. U-Net), which may be trained for particular image types, fluorescence levels, or biofilm morphologies, to give extremely high semantic segmentation accuracy in our experience.Īfter any automated segmentation of the biofilm biovolume, we recommend a visual inspection of the accuracy of the segmentation result, which is displayed in the BiofilmQ user interface.įollowing the detection of the biofilm biovolume, BiofilmQ can dissect the biofilm biovolume into cubes of a user-defined size (which may have the same size as cells). If users choose to import pre-segmented images, we recommend general-purpose segmentation software tools such as ilasik or convolutional neural networks (e.g. Import of pre-segmented images into BiofilmQ. Semi-manual segmentation supported by immediate visual feedback. To perform accurate biofilm biovolume segmentation for a wide variety of image types and signal levels, BiofilmQ includes three different segmentation options:Īutomatic segmentation via classical algorithms, such as Otsu, Ridler-Calvard, robust background, or maximum correlation thresholding. The segmentation quality can have a large impact on the analysis results. Segmentation for Case 1 (biovolume detection followed by cube segmentation): ¶Īn important step in the analysis is that BiofilmQ must be able to identify the biovolume of the biofilm. ![]() This option was added in the 2023 release of BiofilmQ version 1.0.0 and is the only way to analyze single-cell properties. All subsequent analysis (parameter calculation, visualization…) can then be performed as usual based on the imported segmentation. In this case, you can import your segmentation into BiofilmQ using the label image approach. our StarDist OPP tool) and would like to analyze single-cell data based on this segmentation. Using this cube-based approach, it is possible to analyze the biofilm-internal structure, by performing biofilm image cytometry (analogous to flow cytometry, but with spatial features), based on the quantification of many parameters for pseudo-cells.Ĭase 2: You already have a single-cell segmentation from another tool (e.g. Each cube is then treated as a single pseudo-cell for analysis purposes, for which fluorescence, architectural, spatial, and many more properties are measured. during which the 3D biofilm biovolume is detected via thresholding and divided it into cubic pseudo-cells. In this case, you can use the cube-based segmentation of BiofilmQ. The Segmentation tab allows you to set all parameters to extract 3D objects from the imported microscope data.īiofilmQ has two approaches for quantifying biofilm properties in space and time, which are based on segmentation of the biofilm images:Ĭase 1: The properties you are interested in do not require single cell detection or your image resolution is not sufficient to detect individual cells. ![]()
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