#imagej — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #imagej, aggregated by home.social.
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From @nvladimus : "We have opened the #mesoSPIM official User Forum" at https://forum.image.sc/tag/mesospim alongside #Fiji #ImageJ #ilastik #BiaPy #napari #DeepLabCut and many other open source softwares for bioimage informatics.
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From @nvladimus : "We have opened the #mesoSPIM official User Forum" at https://forum.image.sc/tag/mesospim alongside #Fiji #ImageJ #ilastik #BiaPy #napari #DeepLabCut and many other open source softwares for bioimage informatics.
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From @nvladimus : "We have opened the #mesoSPIM official User Forum" at https://forum.image.sc/tag/mesospim alongside #Fiji #ImageJ #ilastik #BiaPy #napari #DeepLabCut and many other open source softwares for bioimage informatics.
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From @nvladimus : "We have opened the #mesoSPIM official User Forum" at https://forum.image.sc/tag/mesospim alongside #Fiji #ImageJ #ilastik #BiaPy #napari #DeepLabCut and many other open source softwares for bioimage informatics.
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From @nvladimus : "We have opened the #mesoSPIM official User Forum" at https://forum.image.sc/tag/mesospim alongside #Fiji #ImageJ #ilastik #BiaPy #napari #DeepLabCut and many other open source softwares for bioimage informatics.
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Light-Sheet Image Analysis Workshop 2026
The Light-Sheet Image Analysis Workshop is a five-day intensive course that will take place in Santiago, Chile, from January 5–9, 2026, designed for students and researchers who wish to gain foundational skills in the processing and analysis of light-sheet microscopy imaging data.
The application deadline has been extended until August 8, 2025! The workshop is free to attend and travel fellowships are available. Learn more and apply here:
https://lightsheetchile.cl/light-sheet-image-analysis-workshop-2026-2/
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URL: https://brunovellutini.com/posts/light-sheet-image-analysis-workshop-2026/ -
This is great spatially-resolved transcriptomics by the R/Bioconductor package visiumStitched, which facilitates stitching the images together with Fiji (ImageJ). 🦾
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CW: Dissertation - progress
Bin heute mit den Automatismen von #ImageJ / #Fiji nicht vollends zufrieden gewesen. Wahrscheinlich sind meine Partikel zu klein.
Jetzt habe ich das manuell erledigt.Hier lohnt sich mal wieder die Stifteingabe.
Habe insgesamt über 300 Partikel analysiert, das genügt für die grobe Statistik und ich kann zeigen, dass die so grob 10 nm Durchmesser haben.
#MöphDissEdit: Das zweite Bild ist nur ein Zwischenstand.
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Tried the same with a more realistic 3D stack from the #ImageJ sample library.#Cellpose runs fast and segments very well out of the box.#CellSeg3D takes considerably longer and seems to segment decently, but I couldn’t get a proper instance #segmentation in the post-processing step (which is recommended as part of its workflow). However, #CellSeg3D looks very promising — just needs some more time and parameter exploration, I guess.
I’d recommend giving it a try 👌
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Tried the same with a more realistic 3D stack from the #ImageJ sample library.#Cellpose runs fast and segments very well out of the box.#CellSeg3D takes considerably longer and seems to segment decently, but I couldn’t get a proper instance #segmentation in the post-processing step (which is recommended as part of its workflow). However, #CellSeg3D looks very promising — just needs some more time and parameter exploration, I guess.
I’d recommend giving it a try 👌
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Tried the same with a more realistic 3D stack from the #ImageJ sample library.#Cellpose runs fast and segments very well out of the box.#CellSeg3D takes considerably longer and seems to segment decently, but I couldn’t get a proper instance #segmentation in the post-processing step (which is recommended as part of its workflow). However, #CellSeg3D looks very promising — just needs some more time and parameter exploration, I guess.
I’d recommend giving it a try 👌
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Tried the same with a more realistic 3D stack from the #ImageJ sample library.#Cellpose runs fast and segments very well out of the box.#CellSeg3D takes considerably longer and seems to segment decently, but I couldn’t get a proper instance #segmentation in the post-processing step (which is recommended as part of its workflow). However, #CellSeg3D looks very promising — just needs some more time and parameter exploration, I guess.
I’d recommend giving it a try 👌
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Tried the same with a more realistic 3D stack from the #ImageJ sample library.#Cellpose runs fast and segments very well out of the box.#CellSeg3D takes considerably longer and seems to segment decently, but I couldn’t get a proper instance #segmentation in the post-processing step (which is recommended as part of its workflow). However, #CellSeg3D looks very promising — just needs some more time and parameter exploration, I guess.
I’d recommend giving it a try 👌
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I promised myself a while back that I wouldn't write any more #ImageJ Macro code. When I made this promise I forgot about all the scripts that we have in use. “Oh, this one just needs a few tweaks and then it can be used in another project." And here I am again in ijm hell!
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I promised myself a while back that I wouldn't write any more #ImageJ Macro code. When I made this promise I forgot about all the scripts that we have in use. “Oh, this one just needs a few tweaks and then it can be used in another project." And here I am again in ijm hell!
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I promised myself a while back that I wouldn't write any more #ImageJ Macro code. When I made this promise I forgot about all the scripts that we have in use. “Oh, this one just needs a few tweaks and then it can be used in another project." And here I am again in ijm hell!
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I promised myself a while back that I wouldn't write any more #ImageJ Macro code. When I made this promise I forgot about all the scripts that we have in use. “Oh, this one just needs a few tweaks and then it can be used in another project." And here I am again in ijm hell!
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I promised myself a while back that I wouldn't write any more #ImageJ Macro code. When I made this promise I forgot about all the scripts that we have in use. “Oh, this one just needs a few tweaks and then it can be used in another project." And here I am again in ijm hell!
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Tips from the Blog XVII: better Process Folder template in Fiji
https://quantixed.org/2025/02/16/tips-from-the-blog-xvii-better-process-folder-template-in-fiji/
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Tips from the Blog XVII: better Process Folder template in Fiji
https://quantixed.org/2025/02/16/tips-from-the-blog-xvii-better-process-folder-template-in-fiji/
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Tips from the Blog XVII: better Process Folder template in Fiji
https://quantixed.org/2025/02/16/tips-from-the-blog-xvii-better-process-folder-template-in-fiji/
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Tips from the Blog XVII: better Process Folder template in Fiji
https://quantixed.org/2025/02/16/tips-from-the-blog-xvii-better-process-folder-template-in-fiji/
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Tips from the Blog XVII: better Process Folder template in Fiji
https://quantixed.org/2025/02/16/tips-from-the-blog-xvii-better-process-folder-template-in-fiji/
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Am Zukunftstag hatte ich eine interessierte Besucherin hier im Labor.
Sie hat eine munzig kleine Spinne aus dem Feuerholzstapel zuhause mitgenommen (der Hinterkörper der Spinne ist 1.7 mm dick).
Diese haben wir in etwas Schaumstoff eingebettet und dann auf unserem #Bruker #SkyScan 2214 aufgenommen (mit 3 um Pixelgrösse).
Dann haben wir die Tomographiedaten angeschaut und in #ImageJ an den Schnittbildern ein paar Dinge daran gemessen (deshalb wissen wir, das der Hinterkörper 1.7 mm dick ist). -
Yep, it lags a little. But still quite impressive considering that each "pixel" is a 150 MPixel multires image. Overall the total number of 'adressable' pixels in this 2D image are 225x225x150 Mpix = 7.6 Tera pixels. #ImageJ
#Fiji
#BigDataViewer -
Yep, it lags a little. But still quite impressive considering that each "pixel" is a 150 MPixel multires image. Overall the total number of 'adressable' pixels in this 2D image are 225x225x150 Mpix = 7.6 Tera pixels. #ImageJ
#Fiji
#BigDataViewer -
Yep, it lags a little. But still quite impressive considering that each "pixel" is a 150 MPixel multires image. Overall the total number of 'adressable' pixels in this 2D image are 225x225x150 Mpix = 7.6 Tera pixels. #ImageJ
#Fiji
#BigDataViewer -
Some people are just nuts. I love it.
See what this guy did with the #ImageJ
macro language.https://www.reddit.com/r/ImageJ/comments/1gfbnkq/strange_fiji_macro_shows_weird_video_feed/
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#µManager: #OpenSource software for control & #automation of #microscope #hardware:
-integrated with #ImageJ
-works with #Leica, #Nikon, #Olympus & #ZeissWeb: https://micro-manager.org/
Github: https://github.com/micro-manager#DIYbio #lab #instruments #MicroManager #microscopy #imaging
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If I do a PR to fix this one-letter typo in #ImageJ is everybody going to think I have lost the plot.
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I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji
Even running on a laptop, once loaded, it's incredibly quick.
Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.
Time to play around with some #Microscopy and #DigitalPathology data!
Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/ -
I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji
Even running on a laptop, once loaded, it's incredibly quick.
Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.
Time to play around with some #Microscopy and #DigitalPathology data!
Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/ -
I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji
Even running on a laptop, once loaded, it's incredibly quick.
Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.
Time to play around with some #Microscopy and #DigitalPathology data!
Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/ -
I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji
Even running on a laptop, once loaded, it's incredibly quick.
Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.
Time to play around with some #Microscopy and #DigitalPathology data!
Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/ -
I am honestly floored at the #SegmentAnything implementation for #ImageJ / #Fiji
Even running on a laptop, once loaded, it's incredibly quick.
Moreover, it's a super-simple install which is a major barrier to many #AI #DeepLearning implementations.
Time to play around with some #Microscopy and #DigitalPathology data!
Details here: https://github.com/segment-anything-models-java/SAMJ-IJ
Photo source: https://www.pexels.com/photo/photo-of-railway-on-mountain-near-houses-1658967/ -
Monster Movie: making movie files from microscopy data
https://quantixed.org/2024/03/19/monster-movie-making-movie-files-from-microscopy-data/
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#PatternJ: An #ImageJ #OpenSource toolset for the #automated & quantitative #analysis of regular spatial #patterns found in #cells & #tissue:
-video #tutorial available
https://doi.org/10.1101/2024.01.17.576053
#DIYbio #lab #instruments #imaging #Fiji #biology #microscopy #bioinformatics -
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/index3D/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 RAMImage 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/ -
@blog I write about biology, imaging, coding, music, and other things I find interesting to share.
Here are some keywords from the latest posts:
#Science #Biology #Outreach #Cifonauta #MarineInvertebrates #Flies #EvoDevo #DevBio #Wnt #Embryo #Larva #ImageJ #ImageProcessing #SciArt
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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/index3D/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 RAMImage 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/index3D/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 RAMImage 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/index3D/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 RAMImage 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/ -
From Oliver Burri via #ImageJ mailing list:
SwitZerland’s Image and Data Analysis School #ZIDAS2024
At ETH Zurich from June 23th (Yes, Sunday!) to June 28th 2024.
Are you a life scientist working with microscopy images?
Do you feel like you could use a nudge in the right direction in order to dive into quantitative image analysis ?
Want to learn to program in ImageJ macro language, use novel deep learning tools, discover best practices in image processing all while working on your own data?The program will focus on hands-on work using the best open source tools available to the BioImage Analysis community.
Registration is open until 2024-03-16.
