QuPath v0.2.0-m5 now available

The fifth milestone on the way to QuPath v0.2.0 is now available here, with many improvements to the pixel classifier, ROIs, annotations, scripting and more.

Don’t be put off by the ‘path’ if you don’t work in pathology: QuPath is designed not just for whole slide images and pathology, but also for other kinds of bioimage analysis.

More details about the latest update in this blog post.

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Indeed, here is a quick gif of performing clustering/cell density analysis on a time lapse! Converted to a movie in FIJI. And with that newfangled Svidro2 color map (colors represent nearby neighbor count).
Red%20vs%20Green

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“Dark Mode” is awesome :boom::100:, thank you.

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Glad it is updated so quickly with another set of amazing tools! I love QuPath so much. I think it is one of my favorite software program I have ever used. Easy to learn for a nonprogrammer like myself. I think I am using only some basic tools now, but I wish to learn to use the AI part of the program, soon. Million thanks to Dr. Pete Bankhead and your team.

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Glad to see the pixel classifier is up and running quite well! Now that we can save the classifier, is there also a way to load the classifier and convert to annotations by script or is it only possible through the GUI so far?

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No, that’s still on the list of things to do: https://petebankhead.github.io/qupath/2019/11/02/fifth-milestone.html#limitations--plans

Actually, after doing some spelunking into the source code I came up with this script which seems to get the job done!

import qupath.lib.gui.ml.PixelClassifierTools

//Need a full image annotation to begin with
createSelectAllObject(true)
def annotations = getAnnotationObjects()

//Define pixel classifier
def project = getProject()
def classifier = project.getPixelClassifiers().get('2019-11-04 Pixel Model')

//Define image data
def imageData = getCurrentImageData()

//Convert pixel classifier to annotations
//Not sure if the smallest annotations/holes is in pixels or microns....
PixelClassifierTools.createAnnotationsFromPixelClassifier(imageData, classifier, annotations, 500, 500, false, true)

//Remove our starting annotation
removeObjects(annotations, true)
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Great work as always Pete!

I’m really looking forward to diving into the new scripting. Since I’m utilizing QuPath from a GUI viewed within the microscope eyepiece while the user is viewing a slide, QPEx looks like a much more friendly way to utilize QuPath classes without needing a different button for each individual algorithm.

I know how I’m spending my weekend :grinning:

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13 posts were split to a new topic: Question about Positive Cell Detection