Hi @andreped

Thanks for this great software and workflow. I would like to generate a model which classifies the epithelia in the colon from WSIs into villi (usually at the boundary) and crypts (circular structures). Essentially, there would be 3 classes,

• background
• villi and
• crypts

My plan is to use your models to generate annotations from my WSIs, import into QuPath, correct them, and then split annotations into different classes.

How do I configure image export for 3 different classes from QuPath, train in MIB and then run on FastPathology?

Is it possible to do this and is this a good workflow idea?

Cheers Pradeep

As our TileImporter script does not support multi-class, the alternative method is to run predictions on WSI level.

This requires you to do something slightly different when running predictions from what was done in the tutorial video.

However, I don't see that there is any documentations for this. This should be added to assist users.

Hi @andreped When I try the import pyramidal tiff script to improve annotations generated by FastPathology, the annotations are smaller by 4 times compared to the WSI. I can't enter a value to control for downsample in the new script anymore. It looks like the script has been updated compared to the on in the video I've reused the old version of the script to import annotations for now .

Cheers Pradeep

First some context: I am attempting to obtain multi-class segmentation of cerebellum tissue with different cellular layers corresponding to separate classes. I have annotated four classes: 'EGL', 'Molecular layer', 'IGL', and 'WM' on QuPath. I tested export tiles with multiple classes from QuPath using two the two different scripts available. Here are the issues I am having with both:

1. The script by @andreped is giving me an error:

ERROR: MissingMethodException at line 61: No signature of method: qupath.lib.images.writers.TileExporter.labeledServer() is applicable for argument types: (qupath.lib.images.servers.LabeledImageServer$Builder) values: [qupath.lib.images.servers.LabeledImageServer$Builder@7bf3c609] Possible solutions: labeledServer(qupath.lib.images.servers.ImageServer)

ERROR: org.codehaus.groovy.runtime.ScriptBytecodeAdapter.unwrap(ScriptBytecodeAdapter.java:70) org.codehaus.groovy.runtime.callsite.PojoMetaClassSite.call(PojoMetaClassSite.java:46) org.codehaus.groovy.runtime.callsite.CallSiteArray.defaultCall(CallSiteArray.java:47) org.codehaus.groovy.runtime.callsite.AbstractCallSite.call(AbstractCallSite.java:125) org.codehaus.groovy.runtime.callsite.AbstractCallSite.call(AbstractCallSite.java:139) Script16.run(Script16.groovy:62) org.codehaus.groovy.jsr223.GroovyScriptEngineImpl.eval(GroovyScriptEngineImpl.java:317) org.codehaus.groovy.jsr223.GroovyScriptEngineImpl.eval(GroovyScriptEngineImpl.java:155) qupath.lib.gui.scripting.DefaultScriptEditor.executeScript(DefaultScriptEditor.java:982) qupath.lib.gui.scripting.DefaultScriptEditor.executeScript(DefaultScriptEditor.java:914) qupath.lib.gui.scripting.DefaultScriptEditor.executeScript(DefaultScriptEditor.java:829) qupath.lib.gui.scripting.DefaultScriptEditor$2.run(DefaultScriptEditor.java:1345) java.base/java.util.concurrent.Executors$RunnableAdapter.call(Unknown Source) java.base/java.util.concurrent.FutureTask.run(Unknown Source) java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) java.base/java.lang.Thread.run(Unknown Source)

2. The script by @pr4deepr is yielding what I need (see below), however, many of the tiles are just background. Is there a way to incorporate the glassthreshold in the script by @andreped to avoid the tiles with too much background? Additionally, in order to get this script to work, I had to edit this line to .multichannelOutput(false).

The current importTiles script does not seem to support importing patches of multiple labels. This one is quite annoying to add multi-class support for, but is something we could try to do early next week? Let us schedule a session.

There have been implemented multi-class variants for the other scripts (i.e., exportTiles and importPyramidalTIFF), but these have not been tested in various scenarios. Currently, these are split into separate scripts; one for single-class and one for multi-class, but I think the multi-class scripts might actually handle the single-class situation as well. Could you run some checks in your pipeline to see if the multi-class scripts are sufficient?

This was observed and discussed in another Issue in the FastPathology repo: https://github.com/AICAN-Research/FAST-Pathology/issues/45

Opening an Issue here to track the issue.

This is likely due to the changed in how the predictions are stored on disk in the new FastPathology version v1.0.1.

Currently, the current download count in the download shield is fixed.

That is because there was no easy way to catch the current count through markdown.

I attempted to create a JS and Python solution for doing that, which were to be run periodically through github actions, but that would result in lots of redundant commits, that are not relevant for the user.

I'll have it fixed until I have time to come up with a better solution.

The main change is multichannelOutput is set to true and there is a label export for each threshold.

// Create an ImageServer where the pixels are derived from annotations
//added option for multi-class export
def labelServer = new LabeledImageServer.Builder(imageData)
    .backgroundLabel(0, ColorTools.WHITE) // Specify background label (usually 0 or 255)
    .downsample(downsample)    // Choose server resolution; this should match the resolution at which tiles are exported
    .addLabel('Epithelia', 1)      // Choose output labels (Define Threshold for each value for each label)
    .addLabel('Crypt', 2)
    .multichannelOutput(true)  // If true, each label is a different channel (required for multiclass probability)
    .build()

Ideally, a loop to run through each label would be nice, so we don't have to define it manually

The current tileImport script does not support multiclass tiles, that is produced prediction tiles generated from MIB by a trained multi-class model.

It is possible to import predictions from MIB to QuPath by predicting on the full WSI in MIB which produces a stitched prediction image, which can be imported in QuPath using the importStitchedTIFfromMIBscript, similarly as done for FastPathology. And therefore, for the full pipeline a tile importer is not critical for using our pipeline.

However, there are scenarios where having a tile importer is beneficial over the other alternative, for instance if one only wishes to run prediction on a smaller region of the WSI (based on segmentations in QuPath) - which can be especially helpful if the image is extremely large. This can make inference a lot faster.

It is possible to do such a workflow in FastPathology, given that you provide the region of interest to run inference on. However, no such method has been made easily accessible. It would also require annotations or a model that can produce these ROI segmentation to use as a preprocessing step for the next patch-wise model.

Therefore, having multi-class support for the tile importer could be a valuable feature.