Often, we want to create annotations around regions that are clearly different from their surroundings: either brighter or darker.
The Wand tool helps, but still involves manually drawing. Setting a threshold is usually a better way.
Why do I need to know this?
Thresholding may become one of the QuPath commands you use most often. It provides a way to define regions of interest without needing to manually annotate everything.
Thresholders in QuPath
At its most basic level, thresholding distinguishes between two classes of pixels: those with values above the specified threshold, and those with values below.
However, there are a few things that complicate matters:
If your image is very large – which is often the case in QuPath – you probably don’t want to apply the threshold at every pixel… there could be billions of them, and it would take a long time, a lot of memory, and result in horribly intricate regions being detected.
Thresholding (at least simple thresholding) assumes ‘one’ value being thresholded for each pixel coordinate… but if your image has multiple channels, then there might effectively be multiple values for each coordinate
Images aren’t perfect, and original pixel values are often noisy. Thresholding them directly can give a messy, fragmented result.
A thresholder in QuPath is something that can apply a threshold to an image while addressing all of these problems.
Thresholds in action
You can create a thresholder using thecommand.
There are three main purposes for this: to create objects (usually annotations), to add measurements to objects (can be anything), and to classify objects (only detections).
The most common of these is to create objects, so it is the one we will worry about first.
A thresholder in QuPath is a special example of a pixel classifier… which is why you’ll see both terms appearing.
When running the command, you’ll be confronted by the following dialog.
You can adjust a number of parameters:
Resolution: Defines the resolution of the image that is thresholded – and thus how ‘blocky’ the output is. This will almost always be lower than the full resolution of the image. In general, choose the lowest resolution that you consider ‘accurate enough’.
Channel: Defines the image channel that will be thresholded. This can also include channels that have been generated computationally, e.g. by color deconvolution, or averaging other channels. Use the Brightness/Contrast pane to visualize how some of these channels look; you want one that gives good contrast for what you want to detect.
Prefilter & Smoothing sigma: Defines how the image is smoothed before thresholding. You probably want to choose ‘Gaussian’ and explore a few sigma values (e.g. 0–5).
Threshold: Defines the threshold; pixels with values above and below can be treated differently. To get an idea of a suitable threshold, move the cursor over the image and check the values shown in the bottom right of the viewer.
Above threshold: Defines the classification of the objects created from pixel values above the threshold. You can leave it blank, or choose ‘Unclassified’ if you don’t want to detect anything above the threshold.
Below threshold: Defines the classification of the objects created from pixel values below the threshold. You can leave it blank, or choose ‘Unclassified’ if you don’t want to detect anything below the threshold.
Region: This doesn’t impact the results; rather, it controls how much of the image QuPath thresholds when you are adjusting settings and previewing the results. Rather than thresholding the whole image (especially if your resolution is high), you can restrict QuPath to threshold only around objects or annotations.
When you adjust these settings, you should see an overlay that color-codes pixels according to the above/below threshold classifications.
You can toggle the overlay on and off by pressing the C button in the toolbar or C shortcut key (for classification… for reasons we shall see later).
You can adjust the overlay opacity using the slider at the top, or by scrolling with the Ctrl or Cmd key pressed.
The settings shown in the screenshot are a reasonable starting point for any brightfield whole slide image – then adjust the smoothing and threshold if you need to.
Saving your thresholder
Once you are happy with the look of the preview overlay, it’s time to do something with it.
However, before you can, QuPath strongly encourages you to save your thresholder as a classifier. This is for your own good, so that the settings can be recorded and available for scripting later.
The save option may only be available if you are using a project!
The classifier itself will be stored within the project, in a ‘classifiers’ subdirectory. You can copy this directory to a new project if you need to transfer the classifier elsewhere.
Keep QuPath happy by entering a classifier name and pressing Save. The buttons below should become magically activated.
Ok, QuPath will allow you to apply a threshold without saving your thresholder – but this option is hidden under the ⋮ button. Beware that doing this can thwart attempts to generate scripts using the thresholder later.
You can reload a saved thresholder with.
Now you can finally click on Create objects. But your choices are not over.
You can decide whether to create objects across the full image, within annotated regions, or within the selected annotated region.
In this case, because we have no existing annotations, our only option is Full image.
After this, there is just one final set of options to navigate, which offers more fine-grained control over exactly what kind of objects will be created – and the extent to which small fragments and holes will be cleaned up.
To decipher Create objects for ignored classes, see Ignored* classifications`.
(Summary: if you want to create objects for a class that ends with an asterisk, select this option.)
The final result should be the detected tissue regions, as classified QuPath annotations.