Let me introduce: InstanSeg 🦠🔬💻👩‍🔬

This *would* have been a short thread about Thibaut Goldsborough’s PhD work… but he solved too many problems.

Now it's a long thread about 2 preprints, a whole new approach to cell segmentation & #opensource software to make it easy to use Preprint #1: segmenting nuclei in pathology images.

Some methods exist - but it remains a hard problem.

Partly because the images are complex & variable, partly because they are *huge*.

Here’s a 38 GB OpenSlide image. Nuclei are small & there are lots.
doi.org/10.48550/arXiv…

QuPath v0.4.0 is now available!

Download it at qupath.github.io

#opensource #bioimageanalysis #digitalpathology #java #javafx There are so many new things that it'll take some days to describe even just the main ones.

For now, I'll start by mentioning a few of the user interface improvements.

QuPath v0.4.0 is more welcoming than previous versions... and a bit more stylish.

If you want to learn about #bioimageanalysis I've written a free & open textbook that tries to help:
bioimagebook.github.io

Thanks to the wonder of @ExecutableBooks & other modern magic it's not quite like a normal book... (thread)

@OpenEdEdinburgh @NEUBIAS_COST @BioimagingNA First, the book tries to cover the main concepts, independently of any software, in a practical way.

This includes common pitfalls & problems, like data clipping, that can doom analysis from the start (2/n)

A small number of people know the real background story to @QuPath, but most don't.

I didn't plan to ever tell it publicly, until a Google Alert today caught my eye.

A thread about open science & academia 👇 (1/n) The short version is that I single-handedly wrote the software as a postdoc but was blocked from releasing it open-source for years, while the environment in which I was working became increasingly toxic.

I handed in my notice as a last-ditch attempt to see it released. (2/n)

@QuPath #tweetorial 3!

This will build on the last one & take us into cell detection, measurement & classification... with some extra visualization tricks along the way.

There will be some new things from v0.2 too. 1/26

https://twitter.com/petebankhead/status/1295965136646176768

Let's jump in with a familiar example: calculating the % of cells positive for Ki67 (i.e. brown).

To do this:
* Annotate an area of interest
* Run 'Positive cell detection'

QuPath detects the cells & provides the result, often in a matter of seconds. 2/26

@QuPath #tweetorial part 2... this time it's (almost) all annotations. 1/20

https://twitter.com/petebankhead/status/1295744964383694848?s=20

#bioimageanalysis #digitalpathology #opensource #javafx I say 'almost all', because I need to mention the command list early: with 'Ctrl + L' you get a searchable list of everything in the menus.

Having told you that, I can now ignore the menus & focus on shortcuts - safe in the knowledge you can find things if you need them. 2/20

Time for a quick introduction to @QuPath in tweetorial form.

First up: a little background on how QuPath differs from other great #opensource tools for #bioimageanalysis like @FijiSc & @CellProfiler - and how to get started viewing images. 1/12

qupath.github.io QuPath's most obvious distinguishing feature is that it handles whole slide images. These are ultra-large 2D images, often up to 50 GB in size.

Whole slide images are everywhere in #digitalpathology & increasingly common in research. 2/12