We have three neuroscience research software engineering jobs available at @sainsburywellcome.bsky.social, two joint with the Advanced Microscopy Facility.
All positions start ASAP, funded until Sept 2028 in the first instance. Salary £54-64k.
More details: neuroinformatics.dev/get-involved
Weird when your "hobby" can be used by others for their profit.
The traditional way to receive thanks for doing work that has value to others is to take payment 💰. I've been working on a mechanism to do this, currently working for BoneJ+, and easy to set up for other ImageJ plugins @beancounter.ch
How can we make sure community software projects that are delivering value are worth looking after from the developers' perspective?
I've always found that being paid is highly motivating. Now there is a fair way for users to help developers keep going: @beancounter.ch
Taking (paid) requests to make enhancements to @bonej.org. What would make it better for you? Contact me for a quote.
📢 Release alert!
BoneJ ulna-r10 brings much anticipated ROI support to Area/Volume Fraction.
TV (TA) = pixel volume (area) in the ROI
BV (BA) = foreground pixel volume (area) in the ROI
Thanks to Sandra Shefelbine at Northeastern for commissioning development of this much-requested enhancement.
The deadline for applying to be an Associate Editor has been extended to the 8th March!
MEE are looking for expertise across a range of topics, including stable isotopes, ecophysiology, mark-recapture methods and modelling, and more!
Find out more and apply here 👇
buff.ly/MzIXj6U
📢 Release alert!
This one lets *all* Intel GPUs use BoneJ+ 🤖
If you have an Intel Iris Xe, UHD or Arc, support is more robust.
Background:
Intel omitted 64-bit floating point precision (double) in their GPUs since c. 2020: this update uses the universally-supported single-precision float type.
📢 New release!
This one makes BoneJ's GPU-accelerated extensions easier to access from Python by using ImageJ2 / scijava-style wrappers.
Just let the Fiji updater run and grab a voucher from bonej.org/shop
Join me for a tutorial on @bonej.org 17 November 13h00 CET
Could be fixed by determining the total number of slices and start and end slice position, then calculating average slice spacing and interpolating the original image data accordingly. A bit messy. Good on 3D Slicer for detecting, alerting users and sorting it!
A closer read of this paper makes it seem that some of the scans were done in chunks of a few slices each, and that the chunks had different slice spacing from each other. That's a bit of a mess: making a composite assuming equal z-spacing throughout the volume will lead to distortions.
ImageJ/Fiji users will be pleased to know that opening a DICOM stack with File > Import > Image Sequence results in correctly calibrated z pixel spacing.
DICOM slice position (0020,0032) and not slice thickness (0018,0050) fields are used to determine the size of the image stack.
It also speaks to the good scientific hygiene of taking some basic measurements from the physical specimens using tape, ruler or caliper, and including standard objects in the image to use as calibration sanity checks prior to later image analysis.
DON'T ASSUME THE INSTRUMENT IS PROPERLY CALIBRATED
OMG this is a good catch. We wrestled with this a long time ago when setting up Slice Geometry. The correct way to set slice spacing (pixel spacing in z) from a DICOM is to use the slice position info (0020,1041 or 0020,0032) and divide by (stack size - 1). It can be out by not much or quite a bit.
👀
Want to automate workflows in #FIJI?
Don't know where to start?
We (@lankylaste.bsky.social, Alicja Skórkowska and Sara Salgueiro Torres) have added a step-by-step tutorial to the wiki to get you started.
Thanks @ctrue.bsky.social for adding!
imagej.github.io/tutorials/ba...
🔥🔥🔥
Gotta admit, I'm pretty excited about this one. GPU-accelerated Local Thickness, for Tb.Th and Tb.Sp. This is the plugin in the @bonej.org logo, made MUCH faster by re-engineering for GPU parallelism.
Drop me a PM to get a token to try it out for free.
The CPU implementation has a problem of thickness spheres overhanging the input mask by a pixel, introducing some inaccuracy. This GPU implementation has a more accurate Euclidean distance transform giving the sphere-fitting higher fidelity to the input geometry and less orthogonal grid bias.
Small structures are also problematic as ever. But what's the thickness of a 1-pixel-wide line? The answer is much more meaningful for features represented by at least several pixels' thickness (what is several you may ask, well, try it and see...).
Smaller simpler images now complete in a few seconds or less, depending on image size and feature size.
Unconstrained 'outside' is still challenging, but is now handled by treating it as spheres seeded from the image borders with a radius equal to the distance to the nearest feature in the image.
Large structures could make Tb.Sp in particular seem to run forever, or least longer than you would reasonably wait. 😴
Try it now. Big structures are still challenging, but should be done in like 1 hour instead of a week.
📢 Release announcement!
Local Thickness+ brings GPU-accelerated Tb.Th and Tb.Sp, for much faster measurements.
Now out in BoneJ.
Just let the updater run, and grab a token from bonej.org/shop to make it go. Drop us a PM to get a coupon for a free token.
Do you want to work with Michalis and us on a fun and ambitious LSFM, tracking and gamification project?
Apply!!
🤔
🤩
Signed up! Come along and get an intro on BoneJ's features.
📣 We’re hiring, please RT! Fully funded 4-year PhD position in our CVPD group at @informatikaehu.bsky.social!
Research at the intersection of AI, deep learning & bioimage analysis. 🧠🔬
📅 Applications open Oct 6 (deadline: ~Oct 27)
📍 San Sebastian, Spain.
👉 cvpd.github.io/post/2025-09...
