Pointed out by @kislinsk in T27095 but as stated before, it belongs to this documentation task not the multi-label one ;)

Notes for the Region Growing 2D tool:

• The tool operates based on the current level window, so changing the level window to optimize the contrast for the ROI should be encouraged
• Moving the mouse up/down is different from left/right. One direction is for level window width, the other direction moves the level window

Another addition for the documentation:

Watershed tool:

• It would be good if level and threshold are a bit explained. Either by refering to the itk documentation (https://itk.org/Doxygen/html/classitk_1_1WatershedImageFilter.html; see also below) of the filter or by destilling the most imporant information.

• Further point out, that the tool is (due to its implementation) faster if you lower levels. So in doubt you should start with high levels (supposed to undersegment) and then lower the levels till you are happy.

Some notes on filter parameters

Two parameters control the output of this filter, Threshold and Level. The units of both parameters are percentage points of the maximum height value in the input.

Threshold is used to set the absolute minimum height value used during processing. Raising this threshold percentage effectively decreases the number of local minima in the input, resulting in an initial segmentation with fewer regions. The assumption is that the shallow regions that thresholding removes are of of less interest.

The Level parameter controls the depth of metaphorical flooding of the image. That is, it sets the maximum saliency value of interest in the result. Raising and lowering the Level influences the number of segments in the basic segmentation that are merged to produce the final output. A level of 1.0 is analogous to flooding the image up to a depth that is 100 percent of the maximum value in the image. A level of 0.0 produces the basic segmentation, which will typically be very oversegmented. Level values of interest are typically low (i.e. less than about 0.40 or 40% ), since higher values quickly start to undersegment the image.

The Level parameter can be used to create a hierarchy of output images in constant time once an initial segmentation is done. A typical scenario might go like this: For the initial execution of the filter, set the Level to the maximum saliency value that you anticipate might be of interest. Once the initial Update() of this process object has finished, the Level can be manipulated anywhere below the initial setting without triggering a full update of the segmentation mini-pipeline. All that is now be required to produce the new output is a simple relabeling of the output image.

Threshold and Level parameters are controlled through the class' Get/SetThreshold() and Get/SetLevel() methods.