#fijisc — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #fijisc, aggregated by home.social.
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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. -
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#L56The test script:
https://github.com/acardona/scripts/blob/dev/python/imagej/FIBSEM/tests/test_matchNearbyFeatures.py -
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.
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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.
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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.
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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.
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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.
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"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
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"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
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"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
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"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
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"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
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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
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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
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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
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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
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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
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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
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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/ -
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/ -
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.