#imglib2 — Public Fediverse posts

@bagder

128-bit math is ... tricky. We implemented a 128-bit type for n-dimensional arrays to represent images in our #ImgLib2 library for image processing. So many gotchas in defining basic arithmetic operations; at some point in version history all our custom implementations were replaced by java's BigInteger's.
https://github.com/imglib/imglib2/blob/master/src/main/java/net/imglib2/type/numeric/integer/Unsigned128BitType.java

Our motivating use case? Integral images (summed-area table: https://en.wikipedia.org/wiki/Summed-area_table ). But now the 128-bit type has found other uses too.

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

@christianp

Unrelated – yet reminds me of the time we implemented unsigned integer division for 128-bit numbers ... in the end we delegated it to java's BigInteger: https://github.com/imglib/imglib2/blob/master/src/main/java/net/imglib2/type/numeric/integer/Unsigned128BitType.java

#ImgLib2

DeepImageJ 3.0 released—a user-friendly #FijiSc plugin that enables the use of a variety of pre-trained neural networks from the #BioimageModelZoo. Works with small and also very large images thanks to #ImgLib2.

https://deepimagej.github.io/

#DeepImageJ #PyTorch #TensorFlow #ImageJ #ImageProcessing

DeepImageJ 3.0 released—a user-friendly #FijiSc plugin that enables the use of a variety of pre-trained neural networks from the #BioimageModelZoo. Works with small and also very large images thanks to #ImgLib2.

https://deepimagej.github.io/

#DeepImageJ #PyTorch #TensorFlow #ImageJ #ImageProcessing

DeepImageJ 3.0 released—a user-friendly #FijiSc plugin that enables the use of a variety of pre-trained neural networks from the #BioimageModelZoo. Works with small and also very large images thanks to #ImgLib2.

https://deepimagej.github.io/

#DeepImageJ #PyTorch #TensorFlow #ImageJ #ImageProcessing

DeepImageJ 3.0 released—a user-friendly #FijiSc plugin that enables the use of a variety of pre-trained neural networks from the #BioimageModelZoo. Works with small and also very large images thanks to #ImgLib2.

https://deepimagej.github.io/

#DeepImageJ #PyTorch #TensorFlow #ImageJ #ImageProcessing

DeepImageJ 3.0 released—a user-friendly #FijiSc plugin that enables the use of a variety of pre-trained neural networks from the #BioimageModelZoo. Works with small and also very large images thanks to #ImgLib2.

https://deepimagej.github.io/

#DeepImageJ #PyTorch #TensorFlow #ImageJ #ImageProcessing

@remenca @spla Jo de fet faig servir molt java ... des de python ... implementat a la JVM! Una colla d'exemples per a processament d'imatge: https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/

El jython és un python 2.7, vell, vetust, però fa la feina.

Les llibreries són totes en java, com la #imglib2 que és reboníssima, i que, gràcies a tot de ponts (com el jpype), es pot fer servir des del python convencional. Vegeu imglyb https://github.com/imglib/imglyb i pyimagej https://pypi.org/project/pyimagej/

Every time I need to run an image processing task, the open source software #FijiSc delivers.

The javadocs are up to date https://javadoc.scijava.org/ , the libraries just work – particularly #ImgLib2 https://imagej.net/libs/imglib2/

And the examples of my own tutorial https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/ written in python 2.7 for the #JVM (#jython), despite some being a decade old, they all just work. Grateful every day for the outstanding backwards compatibility plus the new plugins and libraries that continue to grow https://fiji.sc

Many thanks to the many, many developers and maintainers, particularly Curtis Rueden, who is presently cutting out a new release: 2.11.0 https://forum.image.sc/t/plugin-maintainers-can-you-test-fiji-2-11-0/78852/18

#ImageProcessing

@b0rk For fast integer modulo computation in #java programs. For example, in the #ImgLib2, a type-, dimension-, and storage-independent high-performance image processing library. Here, an example in a pixel type with 4 bits only:

// Same as (i * 4) % 64
final long shift = ( j << 2 ) & 63;

From the Unsigned4BitType class: https://github.com/imglib/imglib2/blob/master/src/main/java/net/imglib2/type/numeric/integer/Unsigned4BitType.java#L105

@benoitmandelbot How nice–love the colors, looks tropical.

There is an example using the #ImgLib2 library implementing the computation and rendering at arbitrary depth of the #Mandelbrot set.

The main class to run it: https://github.com/imglib/imglib2-tutorials/blob/master/src/test/java/interactive/fractals/MandelbrotDemo.java

The viewer implementation:
https://github.com/imglib/imglib2-tutorials/blob/master/src/main/java/interactive/MandelbrotRealViewer2DExample.java

The Mandelbrot math bits, in a separate #java class: https://github.com/imglib/imglib2-tutorials/blob/master/src/main/java/interactive/fractals/MandelbrotRealRandomAccessible.java

@remenca @spla @marcbeldata

Doncs ...

#BigStitcher https://www.nature.com/articles/s41592-019-0501-0

#BigDataViewer https://www.nature.com/articles/nmeth.3392

#ImgLib2 https://academic.oup.com/bioinformatics/article-abstract/28/22/3009/240540

#CATMAID https://academic.oup.com/bioinformatics/article-abstract/25/15/1984/210794

Elastic volume registration https://www.nature.com/articles/nmeth.2072

#vEM #volumeEM https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=59900&yp=36430.399999999994&xp=32311.00000000001&tool=tracingtool&sid0=1&s0=3

@remenca @spla @marcbeldata

Doncs ...

#BigStitcher https://www.nature.com/articles/s41592-019-0501-0

#BigDataViewer https://www.nature.com/articles/nmeth.3392

#ImgLib2 https://academic.oup.com/bioinformatics/article-abstract/28/22/3009/240540

#CATMAID https://academic.oup.com/bioinformatics/article-abstract/25/15/1984/210794

Elastic volume registration https://www.nature.com/articles/nmeth.2072

#vEM #volumeEM https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=59900&yp=36430.399999999994&xp=32311.00000000001&tool=tracingtool&sid0=1&s0=3

@remenca @spla @marcbeldata

Doncs ...

#BigStitcher https://www.nature.com/articles/s41592-019-0501-0

#BigDataViewer https://www.nature.com/articles/nmeth.3392

#ImgLib2 https://academic.oup.com/bioinformatics/article-abstract/28/22/3009/240540

#CATMAID https://academic.oup.com/bioinformatics/article-abstract/25/15/1984/210794

Elastic volume registration https://www.nature.com/articles/nmeth.2072

#vEM #volumeEM https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=59900&yp=36430.399999999994&xp=32311.00000000001&tool=tracingtool&sid0=1&s0=3

@remenca @spla @marcbeldata

Doncs ...

#BigStitcher https://www.nature.com/articles/s41592-019-0501-0

#BigDataViewer https://www.nature.com/articles/nmeth.3392

#ImgLib2 https://academic.oup.com/bioinformatics/article-abstract/28/22/3009/240540

#CATMAID https://academic.oup.com/bioinformatics/article-abstract/25/15/1984/210794

Elastic volume registration https://www.nature.com/articles/nmeth.2072

#vEM #volumeEM https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=59900&yp=36430.399999999994&xp=32311.00000000001&tool=tracingtool&sid0=1&s0=3

@markkitti @HHMI Great to hear about the support for developing #imglib2 from #HHMI and #CZI! Tobias Pietzsch has made outstanding contributions to #imglib2 and #FijiSc in general, and now this support means he will have the means to continue to do so. Excellent!

#FIJI development is mainly concentrated at #LOCI based at #UWMadison.

#imglib2 development is currently focused at @HHMI Janelia where I work.

Tobias Pietzsch, an independent developer, is now picking up of #imglib2 and #BigDataViewer development with support from #CZI and @HHMI .

#FIJI development is mainly concentrated at #LOCI based at #UWMadison.

#imglib2 development is currently focused at @HHMI Janelia where I work.

Tobias Pietzsch, an independent developer, is now picking up of #imglib2 and #BigDataViewer development with support from #CZI and @HHMI .

#FIJI development is mainly concentrated at #LOCI based at #UWMadison.

#imglib2 development is currently focused at @HHMI Janelia where I work.

Tobias Pietzsch, an independent developer, is now picking up of #imglib2 and #BigDataViewer development with support from #CZI and @HHMI .

#FIJI development is mainly concentrated at #LOCI based at #UWMadison.

#imglib2 development is currently focused at @HHMI Janelia where I work.

Tobias Pietzsch, an independent developer, is now picking up of #imglib2 and #BigDataViewer development with support from #CZI and @HHMI .

#FIJI development is mainly concentrated at #LOCI based at #UWMadison.

#imglib2 development is currently focused at @HHMI Janelia where I work.

Tobias Pietzsch, an independent developer, is now picking up of #imglib2 and #BigDataViewer development with support from #CZI and @HHMI .

One of the open source projects I work on is #FIJI (is just #ImageJ).

At the heart of #FIJI is #imglib2, a n-dimensional numerical processing library for #Java.

Yesterday, we had a Chan Zuckberg Initiative Essential Open Source Software sponsored community meeting:
https://forum.image.sc/t/bigdataviewer-imglib2-developer-community-activities/73836

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/

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/