#fijisc — Public Fediverse posts

@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.

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.

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.

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.

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.

@jonny We did that in #FijiSc as a pre-import of all ImageJ and TrakEM2 classes. Convenient it was. Powers that be deemed it too fragile, and was eventually deprecated.

@jim

Kind of shocked but very pleased to see my colleague and #FijiSc co-founder Johannes Schindelin ('dscho') in the photo of that first git meet up. Around that time Johannes taught me to use git.

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

#neuroscience #microglia #imageanalysis #microscopy #FijiSc

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

#FijiSc #java #jython #volumeEM #vEM

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/

#ImageProcessing

Now onto #FijiSc: Fiji is a recursive acronym meaning "Fiji is just ImageJ" https://fji.sc (and the paper https://www.nature.com/articles/nmeth.2019 ) –and #ImageJ is a #java open source software for image processing https://imagej.nih.gov/ij/index.html written by Wayne Rasband from the #NIH Research Branch.

An analogy: think of ImageJ as the kernel and Fiji as the rest of the operating system.

#FijiSc brings to #ImageJ:
(1) a package manager to install and update plugins, and that crucially enables reproducible science by exporting the whole set of plugins and libraries as an executable;
(2) a Script Editor https://imagej.net/scripting/script-editor supporting many languages (#python, #groovy #ruby #scala #clojure and more), all with access to a huge collection of #JVM libraries;
(3) huge amount of libraries such as #ImgLib2, #JFreeChart for plotting, for GUIs, etc.

There are many, many plugins. A tiny sample:

Machine learning-based image segmentation:
- #LabKit https://imagej.net/plugins/labkit/
- #WEKA Trainable Segmentation https://imagej.net/plugins/tws/index

3D/4D/ND Visualization:
- 3D/4D Viewer #3DViewer https://imagej.net/plugins/3d-viewer/index with ray-tracing, orthoslices, volume rendering, and more
- #BigDataViewer #BDV https://imagej.net/plugins/bdv/index for interactively navigate N-dimensional image volumes larger than RAM

Image registration and serial section alignment:
- #BigStitcher for registering 3D/4D tiled datasets, with multiview deconvolution and more https://imagej.net/plugins/bigstitcher/index
- #TrakEM2 for montaging in 2D and alinging in 3D collections of serial sections, typically from #vEM (volume electron microscopy) https://syn.mrc-lmb.cam.ac.uk/acardona/INI-2008-2011/trakem2.html
- #mpicbg libraries for extracting #SIFT and #MOPS features, then finding feature correspondences and estimating rigid and elastic transformation models https://www.nature.com/articles/nmeth.2072

Summarizing #FijiSc is impossible. See the online forum where questions find answers by the hand of the broader community of users and developers https://forum.image.sc/

To fill in my profile tags, a thread:

#TrakEM2 is open source software mostly for #connectomics (but found uses well beyond), and provides the means for both manual and automatic montaging and aligning overlapping 2D image tiles (with #SIFT features and rigid or elastic transformation models), and then reconstructing with mostly manual means–by painting with a digital brush–the volumes of structures of interest, as well as trace the branched arbors of e.g., neurons and annotate their synapses, therefore mapping a #connectome from #vEM (volume electron microscopy).

#TrakEM2 paper at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0038011

Git repository at https://github.com/trakem2/

For 3D visualization, #TrakEM2 uses the 3D Viewer https://imagej.net/plugins/3d-viewer/

As software, #TrakEM2 runs as a plugin of #FijiSc https://fiji.sc/ and in fact motivated the creation of the #FijiSc software in the first place, to manage its many dependencies and therefore facilitate distribution to the broader #neuroscience community.

#TrakEM2 was founded in 2005, when terabyte-sized datasets were rare and considered large. The largest dataset that I've successfully managed with #TrakEM2 was about 16 TB. For larger datasets, see #CATMAID below.

A connectome of the optic lobe of the extremely tiny fairy wasp, Megaphragma sp.

"A complete reconstruction of the early visual system of an adult insect", by Chua et al. 2023 (Chklovskii & Polilov) https://www.sciencedirect.com/science/article/pii/S096098222301237X

Don't miss the supplemental figures.

"Compared with the honeybee and the fruit fly, Megaphragma exhibits the following miniaturization-related adaptations: a significant reduction in the number of ommatidia, absence of several cell types, reduced size, and denucleation of neurons. Interestingly, the reduction in lens diameter is less than that expected from the optimization of the optical resolution of the eye. This suggests that light sensitivity is a more important
consideration when lens diameter approaches the wavelength of light. The absence of wide-field (or non-columnar) lamina neurons in Megaphragma could be a consequence of the smaller number of ommatidia, their larger acceptance angle, and the lower resolving power of the eye."

Volume assembled with #FijiSc and #TrakEM2, and its neurons and synapses mapped with #CATMAID. Woohoo!

#neuroscience #connectomics #VolumeEM #vEM #insects #miniaturization

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

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

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

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

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

Now onto #FijiSc: Fiji is a recursive acronym meaning "Fiji is just ImageJ" https://fji.sc (and the paper https://www.nature.com/articles/nmeth.2019 ) –and #ImageJ is a #java open source software for image processing https://imagej.nih.gov/ij/index.html written by Wayne Rasband from the #NIH Research Branch.

An analogy: think of ImageJ as the kernel and Fiji as the rest of the operating system.

#FijiSc brings to #ImageJ:
(1) a package manager to install and update plugins, and that crucially enables reproducible science by exporting the whole set of plugins and libraries as an executable;
(2) a Script Editor https://imagej.net/scripting/script-editor supporting many languages (#python, #groovy #ruby #scala #clojure and more), all with access to a huge collection of #JVM libraries;
(3) huge amount of libraries such as #ImgLib2, #JFreeChart for plotting, for GUIs, etc.

There are many, many plugins. A tiny sample:

Machine learning-based image segmentation:
- #LabKit https://imagej.net/plugins/labkit/
- #WEKA Trainable Segmentation https://imagej.net/plugins/tws/index

3D/4D/ND Visualization:
- 3D/4D Viewer #3DViewer https://imagej.net/plugins/3d-viewer/index with ray-tracing, orthoslices, volume rendering, and more
- #BigDataViewer #BDV https://imagej.net/plugins/bdv/index for interactively navigate N-dimensional image volumes larger than RAM

Image registration and serial section alignment:
- #BigStitcher for registering 3D/4D tiled datasets, with multiview deconvolution and more https://imagej.net/plugins/bigstitcher/index
- #TrakEM2 for montaging in 2D and alinging in 3D collections of serial sections, typically from #vEM (volume electron microscopy) https://syn.mrc-lmb.cam.ac.uk/acardona/INI-2008-2011/trakem2.html
- #mpicbg libraries for extracting #SIFT and #MOPS features, then finding feature correspondences and estimating rigid and elastic transformation models https://www.nature.com/articles/nmeth.2072

Summarizing #FijiSc is impossible. See the online forum where questions find answers by the hand of the broader community of users and developers https://forum.image.sc/

Now onto #FijiSc: Fiji is a recursive acronym meaning "Fiji is just ImageJ" https://fji.sc (and the paper https://www.nature.com/articles/nmeth.2019 ) –and #ImageJ is a #java open source software for image processing https://imagej.nih.gov/ij/index.html written by Wayne Rasband from the #NIH Research Branch.

An analogy: think of ImageJ as the kernel and Fiji as the rest of the operating system.

#FijiSc brings to #ImageJ:
(1) a package manager to install and update plugins, and that crucially enables reproducible science by exporting the whole set of plugins and libraries as an executable;
(2) a Script Editor https://imagej.net/scripting/script-editor supporting many languages (#python, #groovy #ruby #scala #clojure and more), all with access to a huge collection of #JVM libraries;
(3) huge amount of libraries such as #ImgLib2, #JFreeChart for plotting, for GUIs, etc.

There are many, many plugins. A tiny sample:

Machine learning-based image segmentation:
- #LabKit https://imagej.net/plugins/labkit/
- #WEKA Trainable Segmentation https://imagej.net/plugins/tws/index

3D/4D/ND Visualization:
- 3D/4D Viewer #3DViewer https://imagej.net/plugins/3d-viewer/index with ray-tracing, orthoslices, volume rendering, and more
- #BigDataViewer #BDV https://imagej.net/plugins/bdv/index for interactively navigate N-dimensional image volumes larger than RAM

Image registration and serial section alignment:
- #BigStitcher for registering 3D/4D tiled datasets, with multiview deconvolution and more https://imagej.net/plugins/bigstitcher/index
- #TrakEM2 for montaging in 2D and alinging in 3D collections of serial sections, typically from #vEM (volume electron microscopy) https://syn.mrc-lmb.cam.ac.uk/acardona/INI-2008-2011/trakem2.html
- #mpicbg libraries for extracting #SIFT and #MOPS features, then finding feature correspondences and estimating rigid and elastic transformation models https://www.nature.com/articles/nmeth.2072

Summarizing #FijiSc is impossible. See the online forum where questions find answers by the hand of the broader community of users and developers https://forum.image.sc/

Now onto #FijiSc: Fiji is a recursive acronym meaning "Fiji is just ImageJ" https://fji.sc (and the paper https://www.nature.com/articles/nmeth.2019 ) –and #ImageJ is a #java open source software for image processing https://imagej.nih.gov/ij/index.html written by Wayne Rasband from the #NIH Research Branch.

An analogy: think of ImageJ as the kernel and Fiji as the rest of the operating system.

#FijiSc brings to #ImageJ:
(1) a package manager to install and update plugins, and that crucially enables reproducible science by exporting the whole set of plugins and libraries as an executable;
(2) a Script Editor https://imagej.net/scripting/script-editor supporting many languages (#python, #groovy #ruby #scala #clojure and more), all with access to a huge collection of #JVM libraries;
(3) huge amount of libraries such as #ImgLib2, #JFreeChart for plotting, for GUIs, etc.

There are many, many plugins. A tiny sample:

Machine learning-based image segmentation:
- #LabKit https://imagej.net/plugins/labkit/
- #WEKA Trainable Segmentation https://imagej.net/plugins/tws/index

3D/4D/ND Visualization:
- 3D/4D Viewer #3DViewer https://imagej.net/plugins/3d-viewer/index with ray-tracing, orthoslices, volume rendering, and more
- #BigDataViewer #BDV https://imagej.net/plugins/bdv/index for interactively navigate N-dimensional image volumes larger than RAM

Image registration and serial section alignment:
- #BigStitcher for registering 3D/4D tiled datasets, with multiview deconvolution and more https://imagej.net/plugins/bigstitcher/index
- #TrakEM2 for montaging in 2D and alinging in 3D collections of serial sections, typically from #vEM (volume electron microscopy) https://syn.mrc-lmb.cam.ac.uk/acardona/INI-2008-2011/trakem2.html
- #mpicbg libraries for extracting #SIFT and #MOPS features, then finding feature correspondences and estimating rigid and elastic transformation models https://www.nature.com/articles/nmeth.2072

Summarizing #FijiSc is impossible. See the online forum where questions find answers by the hand of the broader community of users and developers https://forum.image.sc/

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.

#BioimageInformatics

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.

#BioimageInformatics

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.

#BioimageInformatics

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.

#BioimageInformatics

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.

#BioimageInformatics

"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

#ImageProcessing #BioimageInformatics

"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

#ImageProcessing #BioimageInformatics

"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

#ImageProcessing #BioimageInformatics

"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

#ImageProcessing #BioimageInformatics

"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

#ImageProcessing #BioimageInformatics

@neuralreckoning

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

#academia

@posertinlab

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.

#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

#java21 #ImageProcessing #Bioimage

@eamon

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?

@steveroyle

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.

@jonny

#FijiSc can open MRI files, and export them to more accessible formats.

@lana

I suggest #FijiSc: https://fiji.sc

Free and open source, and dedicated to bioimage informatics. Works well in all operating systems.

@koen_hufkens @iris @jonny

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.

"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

#java #DeepLearning #JDLL

@FlyBase Great resource! It's also relatively straightforward to convert red to magenta for the existing red/green images. In #FijiSc, you can do it with “Image > Color > Replace Red With Magenta” (see example below).

This could be an alternative for images whose licenses allow modification. I'm happy to help with the batch conversion if you need!

@jonny

We once referred to this as:

"A problem often related as 'the computer science PhD student moved on, and we do not know what parameters were used, neither what the magic numbers mean'."

https://www.nature.com/articles/nmeth.2082

The #FijiSc project aimed at addressing these issues for bioimage informatics, and has largely succeeded.

@geordie

Here is an old-school image processing tutorial for #FijiSc in #jython 2.7 (python for java):

https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/

Enjoy!

@quantixed

There is a pull request by Robert Haase that introduces #ChatGPT to the #FijiSc Script Editor:

https://github.com/scijava/script-editor/pull/67

Caveat emptor!

@jencmars

For those who can't afford commercial software, and for those who want full flexibility and extensibility and future-proofing the ability to open their own art work years from now, there's free open source software:

* #Krita: replaces Adobe Photoshop for editing and painting; https://krita.org/

* #Inkscape: multi-page, replaces Adobe Illustrator and Adobe InDesign; great for scientific figures and posters; https://inkscape.org/

* #Gimp: image editor, with layers and transparencies; replaces Adobe Photoshop; https://www.gimp.org/

* #MyPaint: just for painting; https://mypaint.app/

* #FijiSc: for image processing and image analysis, with conventional computer vision techniques and also machine learning. Handles multi-dimensional bioimagery. https://fiji.sc

* #Blender: for 3D animation and video editing; https://blender.org

* #ffmpeg: command-line based video editing; see this page https://ffmpeg.lav.io/ for testing out commands.

I don't use anything else: there's no need. And all of the above are extensively documented.

#OpenSourceSoftware #OSS

@carrerassanahuja

#TrakEM2:
* https://syn.mrc-lmb.cam.ac.uk/acardona/INI-2008-2011/trakem2.html
* https://imagej.net/plugins/trakem2/

És un plugin gros de #FijiSc: https://fiji.sc/

Et poso una captura de pantalla d'un fotomontatge automàtic:

@carrerassanahuja També ho pots fer amb #TrakEM2 i #FijiSc — però és més tècnic. Igualment de codi obert.

@steveroyle

I assume you’ve tried #FijiSc 3D Viewer already?