Pete Bankhead Profile picture
Apr 20, 2022 15 tweets 10 min read Read on X
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)
It also includes tricky stuff important for a lot of microscopy image analysis, like noise distributions & the signal-to-noise ratio... (3/n)
...and a whole lot of image processing techniques, including filters, thresholds, morphological operations & other image transforms.

I've tried to not just explain how a technique works, but to give an intuition for what's going on - and warnings about what to look out for (4/n)
And I've included questions, because I find it useful to test my understanding of new stuff as I'm reading it (5/n)
I wanted it to work as a coherent course for anyone who wants to read everything from start to finish, but that could take a while.

Fortunately it's all searchable and cross-linked, so it can also be used for reference (6/n)
But it's no use just knowing the concepts, they need to be applied somehow using software.

So there's lots of info about how all these ideas relate to #ImageJ & @Fiji (7/n)
Some of this appeared in my old 'Analyzing fluorescence microscopy with ImageJ' handbook - but it has been completely revised & even includes some new changes introduced in ImageJ within the past few weeks (8/n)
There's also a brief introduction to coding using ImageJ macros (9/n)
But I know lots of people who code like using Python, with #numpy, #scipy, #skimage & #matplotlib

So there are some sections on that - written as @projectjupyter notebooks that come alive with @mybinderteam

@numpy_team @SciPy_team @matplotlib (10/n)
But really, the whole thing is written using MyST Markdown and Python as a #jupyterbook - which means you can access the Python code used to generate almost all the figures (11/n)
And, through yet more @ExecutableBooks magic, you can even regenerate figures live through the browser

(Just make sure the code is expanded first...) (12/n)
Anyhow, there will undoubtably be many typos & things to improve.
I plan to keep working on it from time to time, but I hope it's already developed enough to be useful.

It's open under a @creativecommons license - check it out at https://bioimagebook.github (13/n)
One last thing: I'm building my research group at @EdinUni_IGC - so if you want to join me in trying to make #bioimageanalysis a bit easier, look out for new postdoc/research software engineer positions being advertised very soon (14/n)
And if anyone wants to hear me talk a little bit about the book & a lot more about the #opensource software I'm also developing, please join me for the @QuPath webinar on 25 April
(15/15)

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More from @petebankhead

Sep 11, 2024
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…



Image
Image
Image
Image
Ideally, you’d have a method that’s fast, accurate, open, user-friendly & runs in the software you want to use - on the computer you’ve got.

Achieving all of those is hard. All methods make tradeoffs. Image
Read 20 tweets
Dec 3, 2022
QuPath v0.4.0 is now available!

Download it at qupath.github.io

#opensource #bioimageanalysis #digitalpathology #java #javafx QuPath homepage
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.
In fact, you can even style it in your own unique way if you really want to, thanks to #javafx & css (ideally in a nicer way than in my screenshots).

qupath.readthedocs.io/en/0.4/docs/re… ImageImage
Read 31 tweets
Feb 5, 2021
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)
This worked - but meant I was out of academia, and my old group were free to take the credit.

Which they did.

It was strange to see people suddenly become huge fans of open science, speaking like they were my biggest supporters rather than the reason I left. (3/n)
Read 10 tweets
Aug 20, 2020
@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

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
In practice, you might want/need to tweak the settings to get better results.

There's a whole YouTube tutorial about that youtube.com/playlist?list=… )

There's also a publication independently comparing QuPath & other software for Ki67 doi.org/10.1038/s41374… 3/26
Read 26 tweets
Aug 19, 2020
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
(If you *really* like the command list, you can turn it into a command bar and give it a special place at the top of the viewer... but I probably wouldn't unless I broke my 'L' key.) 3/20
Read 21 tweets
Aug 18, 2020
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
A single whole slide image can be more than 200k x 100k pixels in size & contain a huge amount of information that matters to researchers & clinicians.

The trouble is trying to wring that information out of billions of pixels. 3/12
Read 12 tweets

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