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 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)
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)
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
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