@pchestek That's bizarre. Thousands of papers using pirated Photoshop for adjusting confocal and gel images for brightness and contrast would have to retracted – from prior to the subscription model of modern Photoshop.
(If you ever want to adjust B&C on an image, just use free open source software #FijiSc instead: https://fiji.sc )
Hehe, the first game of life I code (in #FijiSc
using #BigDataViewer
obviously). Naive implementation, but still functionally defined and lazy computed - see how the data arrives as I scroll, and is cached.
This is a FIJI plugin that can analyze branched structures in a broad range of settings. They started with Microglia, but apparently it's broadly applicable (also works with neurons and even corals). Looks useful for analyzing 2D images.
AutoMorFi: Automated Whole-image Morphometry in Fiji/ImageJ for Diverse Image Analysis Needs
Bouadi ... Tuan Leng Tay, preprint at biorxiv 2024
https://www.biorxiv.org/content/10.1101/2024.07.26.605357v1.full
True as always that the way to make software run faster is to make it do less operations. After all, CPUs can only execute a fixed number of operations per unit of time.
Here, I tweaked code for serial section registration that drops execution time from 27 seconds to 100 milliseconds: a 270x speed up.
All it had to do is to search for matching SIFT features in one image only within a predetermined radius centered on one SIFT feature in another image. Extremely effective for when e.g., the maximum translation is known.
The matching code using a KDTree:
https://github.com/acardona/scripts/blob/master/java/asm/my/PointMatchesFast.java#L56
The test script:
https://github.com/acardona/scripts/blob/dev/python/imagej/FIBSEM/tests/test_matchNearbyFeatures.py
Online course on "Scientific Image Editing and Figure Creation" using open source software #FijiSc and #Inkscape.
By BioVoxxel via Zoom, on:
Thu 27 Jun 2024 09:00 - Fri 28 Jun 2024 15:30 CEST
Register at: https://www.tickettailor.com/events/biovoxxel/1198561
Details: https://www.biovoxxel.de/workshops/scientific-image-editing-and-figure-creation/
To non-faculty for sure. My first move would be to expand funding for PhD students: attract many, and with a good salary to bias the choice away from industry.
It's so cheap to support research work that may very well end up saving millions across the board, e.g., #FijiSc software to name just one close to me: https://albert.rierol.net/tell/20160601_Unintended_consequences_of_untimely_research.html
Image registration for light-microscopy at petabyte scale, an update of the #BigSticher for #FijiSc by @preibischs
https://github.com/JaneliaSciComp/BigStitcher-Spark
Ready for expansion microscopy #ExM approaches to mapping neural circuits and more.
There's an MRC 16-bit image file format reader here for #FijiSc: https://github.com/fiji/IO/blob/master/src/main/java/sc/fiji/io/Open_MRC_Leginon.java
The header includes MRC format details for documentation.
I wrote it.
@adredish @neuralreckoning @brembs @ScholarNexus
Likewise for #TrakEM2, #FijiSc and #catmaid software – except we did write papers for them.
"I would have never thought moving away from a 10yr old JDK could be this smooth!" – Tiago Ferreira, author of the SNT plugin for neuronal tracing among others.
Curtis Rueden pushing forward the release of #FijiSc with #Java21 – a huge upgrade from the decade-old java 8 that Fiji uses today.
Testers are reporting success even in new MacOS chipsets.
https://forum.image.sc/t/jaunch-a-new-java-launcher-test-fiji-with-java-21/92058/1
#FijiSc being prepped to run on #Java 21: give it a try and please report any issues. See the post by Curtis Rueden at the image forum:
https://forum.image.sc/t/jaunch-a-new-java-launcher-test-fiji-with-java-21/92058/1
I routinely run #java code we wrote in 2005–2012. And scripts in jython on top of that written from 2010 onwards. All in #FijiSc – https://fiji.sc for image processing.
Perhaps the #RStats community does not value long-term stability or hasn't adopted backwards compatibility strategies when updating libraries?
See: https://imagej.net/plugins/trakem2/
The brief bit, for #FijiSc:
$ ./ImageJ-linux64 --dry-run | sed 's/-Xincgc/-XX:+UseG1GC -verbose:gc -XX:+PrintGCDateStamps'/ >> launcher.sh
$ chmod +x launcher.sh
$ ./launcher.sh
See also this forum entry:
https://forum.image.sc/t/fiji-with-java-8-use-the-g1-garbage-collector-xx-useg1gc-instead-of-incremental-xingc/88620/4
The difference in performance for us was huge, order of magnitude, and being able to use effectively a lot more RAM.
I suggest #FijiSc: https://fiji.sc
Free and open source, and dedicated to bioimage informatics. Works well in all operating systems.
I find writing documentation relaxing. It's also the best way I have to future-proof my own work: so that I know how I did what down the line. For example, see this labour of love over 13 years, for image processing in #FijiSc: https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/ As far as I know all the scripts run to this day, and it's proven invaluable time and again to myself – and likely to others, which is a win-win.
Why would one want to run machine learning inference from #java?
To do so on 3D, 4D, ND datasets, trivially accessible from image processing and visualization libraries such as #ImgLib2, the #BigDataViewer, #LabKit and more, all integral parts of #FijiSc.
* LabKit: https://imagej.net/plugins/labkit/
* BigDataViewer: https://imagej.net/plugins/bdv/
* ImgLib2: https://imagej.net/libs/imglib2/
* Fiji: https://fiji.sc
"Introducing the Java Deep Learning Library - JDLL"
https://forum.image.sc/t/introducing-the-java-deep-learning-library-jdll/82255
Can run models from #PyTorch, #TensorFlow, and #Onnx in #FijiSc https://fiji.sc and other java-based image processing open source software like Icy https://icy.bioimageanalysis.org
Code: https://github.com/bioimage-io/JDLL
Paper: "JDLL: A library to run Deep Learning models on Java bioimage informatics platforms"
by Carlos Garcia Lopez de Haro et al. 2023
https://arxiv.org/abs/2306.04796 and also https://www.nature.com/articles/s41592-023-02129-x
