#vem — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #vem, aggregated by home.social.
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@innuendo @jernej__s @ZachWeinersmith
Our eFIB-SEM (as per Xu et al. 2017 https://elifesciences.org/articles/25916 ) runs 24/7 for up to 8 weeks, when it runs out of source. And it's essential that it does so, uninterrupted, since it's imaging at nanometre resolution, milling 2 nm slices at a time with an ion beam and then repeatedly imaging the block face. The process is unsurprisingly very sensitive; was hard enough to overcome the reheat cycle of the instrument itself (every 60 hours or so) by detecting it, de-engaging the beams and then seamlessly re-engaging with the block face.
A brief video of the kind of data we acquire:
https://youtu.be/p4MH4-I-P5IThe MS Windows computer deciding to reboot on its own is unacceptable.
#ElectronMicroscopy #Zeiss #FIBSEM #VolumeEM #vEM #Drosophila
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Nuno da Costa at the Allen Institute for Brain Research is hiring:
"We are seeking an exceptional individual to develop innovative sectioning techniques for a large scale Electron Microscopy pipeline."
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Nuno da Costa at the Allen Institute for Brain Research is hiring:
"We are seeking an exceptional individual to develop innovative sectioning techniques for a large scale Electron Microscopy pipeline."
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Nuno da Costa at the Allen Institute for Brain Research is hiring:
"We are seeking an exceptional individual to develop innovative sectioning techniques for a large scale Electron Microscopy pipeline."
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Nuno da Costa at the Allen Institute for Brain Research is hiring:
"We are seeking an exceptional individual to develop innovative sectioning techniques for a large scale Electron Microscopy pipeline."
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Nuno da Costa at the Allen Institute for Brain Research is hiring:
"We are seeking an exceptional individual to develop innovative sectioning techniques for a large scale Electron Microscopy pipeline."
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"SmartEM: machine learning-guided electron microscopy", by Meirovitch et al. 2025 (Shavit's, Samuel's and Lichtmann's lab).
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Das #Lichtsteuergerät SL 3930 wurde um 1988 im VEB Starkstrom-Anlagenbau #Leipzig #Halle #VEM als Ergänzung zur gleichnamigen #HiFi Kombination produziert. Das Gerät kombiniert ein Lauflicht-Steuergerät mit einer klassischen #Lichtorgel. Über die rückseitigen Schuko-Steckdosen können insgesamt 4 Strahler angeschlossen werden. Das #RFT SL 3930 folgte dem bereits in #Berlin produzierten #AKA #Lichteffektgerät.
Die ausführlichen Beschreibungen habe ich unter 🏳️🌈 https://rk7.de/hifi 🏳️🌈 hinterlegt.😊 -
Happy to be involved in UNICIL, our new Wellcome Trust Discovery project coordinated by @micromotility.bsky.social
We will study ciliary dynamics across scales and organisms. A long-term #postdoc position will be available at Heidelberg University @uniheidelberg shortly.
@uniofexeter press release:
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In our new preprint we describe the synaptic #connectome of the nerve net in the ctenophore gravisensory organ. https://www.biorxiv.org/content/10.1101/2025.06.26.661250v1 #biology #neuroscience #ctenophore #vEM
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Arrived at the Volume Electron Microscopy
Gordon Research Conferencehttps://www.grc.org/volume-electron-microscopy-conference/2025/
near Barcelona. I will speak about Volume EM and #connectomics in marine zoomplankton.
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"Comparative connectomics of Drosophila descending and ascending neurons", Tomke Stürner et al. 2025 (Greg Jefferis and Katharina Eichler's labs).
https://www.nature.com/articles/s41586-025-08925-zCompares between males and females.
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V-EM - The Virtual EM Livestreams!
#boost #em #livestreams #vemhttps://www.youtube.com/playlist?list=PLxsJbBDIUBCuxrFsJyCZfmsiKo-_tU_MO
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The revised version of our #Platynereis #connectome paper is now out:
https://elifesciences.org/reviewed-preprints/97964
Cell-type-level annotation of the whole organisms, including synaptic and desmosomal connectomes. Can be explored with CATMAID here:
https://catmaid-jekelylab.cos.uni-heidelberg.de
#larva #marine #neuroscience #vEM -
GridTape TEM with beam deflection to reduce stage movements and increase imaging throughput:
"Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy", Zhao et al. 2024
https://www.nature.com/articles/s41467-024-50846-4
#TEM #GridTapeTEM #vEM #VolumeEM #connectomics #neuroscience #microscopy #ElectronMicroscopy
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Thanks for sharing this – will have to think about it. In our case, we built in-house the gridtape reel holders which amount to a custom stage with a mini-camera in it for identifying slots and controlling imaging. So we don't use the factory-supplied stage.
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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 -
Also check the nice supplementary movies of #vEM reconstructions
https://www.nature.com/articles/s41467-024-48044-3#Sec20 -
New paper from Ikeda et al. on the biogenesis of chitin bristles in the annelid #Platynereis with nice #vEM reconstructions and a chitin synthase knockout.
Bristles are formed in a process of biological 3D printing. @biology
#microscopy
https://www.nature.com/articles/s41467-024-48044-3 -
From Moritz Helmstaedter on #RoboEM:
"In today’s AI research, any project lasting longer than 6 months is considered slow, if not outdated and overrun by history. RoboEM, with a concept and an approach that seem quite plausible, still took 5 years from idea to the fully evaluated tool reported here. This was only possible with the patient support of the Max Planck Society, which encourages long-term projects, and the tenacity of the first author. Details made all the difference. And, as often, dead ends had to be avoided efficiently. We had to abandon, for example, the idea of using steering variability to indicate branch points — a nice analogy to road intersections in car steering, but too far-fetched to work well enough in brain tissue data. Sometimes giving up beloved ideas is as important as following through on others. M.H."
#connectomics #volumeEM #vEM #neuroscience #MaxPlanckSociety
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“Perception: How larvae feel the world around them” by Jimena Berni https://elifesciences.org/articles/96708
… an insight piece on Andreas Thum’s lab work on mapping the sensory organs of the #Drosophila larva with electron microscopy:
“Morphology and ultrastructure of external sense organs of Drosophila larvae”
Richter et al. 2024 https://elifesciences.org/articles/96708 -
Christel Genoud is recruiting an electron microscopy specialist at the University of Lausanne:
https://wp.unil.ch/emf/?trk=feed-detail_main-feed-card-text -
"DeepFocus: fast focus and astigmatism correction for electron microscopy", Schubert et al. 2024 (Kornfeld lab)
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A review of the current state of multimodal bioimaging:
"Integrating cellular electron microscopy with multimodal data to explore biology across space and time", McCafferty et al. 2024 https://www.cell.com/cell/fulltext/S0092-8674(24)00007-2
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Postdoc position at the lab of Michael Winding at #TheCrick in London:
... on neuronal circuits that drive social interactions between animals.
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"SmartEM: machine-learning guided electron microscopy" by Meirovitch et al 2023 (Nir Shavit and Aravi Samuel labs)
https://www.biorxiv.org/content/10.1101/2023.10.05.561103v1.abstractTurn up the imaging rate of your scanning electron microscope by only imaging at high resolution the areas of the image necessary to resolve ambiguities in the low-res image. Motivated by the need for high-throughput but inexpensive imaging for #connectomics.
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Our group has several 3y+ #postdoc openings at the Centre for Organismal Studies at Heidelberg University. Come and join us in Heidelberg to study eye evolution, #cilia, #behaviour by #connetomics #vEM etc. #neuroscience #Evolution #job #science
https://adb.zuv.uni-heidelberg.de/info/INFO_FDB$.startup?MODUL=LS&M1=1&M2=0&M3=0&PRO=34018 -
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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Following from Collinson's paper, here is my take on scaling up volume electron microscopy for connectomics in the #UK or any country willing to commit about 10 to 20 million a year:
"Growing and nurturing a research base in connectomics"
https://albert.rierol.net/tell/20230823_connectomics_research_base.htmlFrom the humble #Drosophila to the #mouse brain, passing through the mosquito, honeybee, gecko lizards and the Etruscan shrew.
It's possible, it's feasible, it's timely: therefore we must.
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"Volume EM: a quiet revolution takes shape" – a review and commentary by Lucy Collinson et al. 2023 on present and future electron microscopy technology and its application https://www.nature.com/articles/s41592-023-01861-8
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Amusing that the piece includes optical microscopy methods unrelated to neural function, such as STED, PALM, expansion microscopy and CLARITY, but ignores electron microscopy methods key to the current boom in #connectomics. In particular, Denk and Horstmann 2004 for SBEM https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0020329 , Knott et al. 2008 for first application of FIBSEM to neuronal tissue https://www.jneurosci.org/content/28/12/2959 , Schalek et al. 2011 for the ATUM serial section tape-collecting system https://academic.oup.com/mam/article-abstract/17/S2/966/6947317 , Xu et al. 2017 for e-FIBSEM and Graham et al. 2019 for GridTape TEM https://www.biorxiv.org/content/10.1101/657346.abstract
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Electron Microscopy Specialist - cryo-CLEM team
CLF Octopus
Looking for #EM specialist to work on a new #cryoCLEM team at #CLFOctopus facility. Join us to work on exciting #MultidisciplinaryScience
See the full job description on jobRxiv: https://jobrxiv.org/job/clf-octopus-27778-electron-microscopy-specialist-cryo-clem-team/?feed_id=48061
#ScienceJobs #hiring #research #vEM, #cryoFIB-S...
https://jobrxiv.org/job/clf-octopus-27778-electron-microscopy-specialist-cryo-clem-team/?feed_id=48061 -
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
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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
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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
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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
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#introduction To fill in my profile tags, a thread:
#TrakEM2 open source software mostly for #connectomics, and supports manual and automatically montaging and aligning overlapping 2D image tiles (with #SIFT features and rigid or elastic transformation models), and reconstructing by painting volumes or tracing branched neuronal arbors neurons plus synapses to map a #connectome from #vEM (volume electron microscopy).
See: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0038011
Git repository at: https://github.com/trakem2/
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@manlius Yes, a lot, but generated mostly with #CATMAID which is more purpose-built for #connectomics.
An early reconstruction of a neural circuit done with #TrakEM2 was by Davi Bock et al. 2011 on the mouse visual cortex, "Network anatomy and in vivo physiology of visual cortical neurons" https://www.nature.com/articles/nature09802
Another one with #TrakEM2 was by Dan Bumbarger et al. 2013 "System-wide rewiring underlies behavioral differences in predatory and bacterial-feeding nematodes" where they compared #celegans with another nematode, #pristionchus pacificus that has the exact same amount of neurons but connected differently https://www.sciencedirect.com/science/article/pii/S0092867412015000
Later ones with #CATMAID include:
The polychaete worm #Platynereis by @jekely 's group, "Whole-animal #connectome and cell-type complement of the three-segmented Platynereis dumerilii larva" Verazto et al. 2020 https://www.biorxiv.org/content/10.1101/2020.08.21.260984v2.abstract
And all of ours in #Drosophila larva. See the #VirtualFlyBrain server which hosts the #vEM of the whole central nervous system and lists all the neurons included in each published paper (currently 23), shared among the papers and all connecting to each other: https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=108250&yp=82961.59999999999&xp=54210.799999999996&tool=tracingtool&sid0=1&s0=2.4999999999999996&help=true&layout=h(XY,%20%7B%20type:%20%22neuron-search%22,%20id:%20%22neuron-search-1%22,%20options:%20%7B%22annotation-name%22:%20%22papers%22%7D%7D,%200.6)
The 24th will come soon, featuring the complete whole #Drosophila larval brain with ~2,500 neurons. It's under review.
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Have you visited the #FlyWire website yet? Both for helping proofread and analyze the whole #Drosophila brain #connectome, or simply to admire the beautiful renderings of neuronal arbors: https://join.flywire.ai
(See also the #VirtualFlyBrain for #ontology-driven navigation of the fly brain, and access to images of genetic driver lines, and more: https://v2.virtualflybrain.org/org.geppetto.frontend/geppetto?id=VFB_00101567&i=VFB_00101567 )
Wish I had time or resources to create such a beautiful landing page for the larval central nervous system. The #connectome of the whole larval brain is coming soon. For now, see the #vEM images and some ~3,000 published neurons in this #CATMAID server: https://l1em.catmaid.virtualflybrain.org/?pid=1&zp=108250&yp=82961.59999999999&xp=54210.799999999996&tool=tracingtool&sid0=1&s0=2.4999999999999996&help=true&layout=h(XY,%20%7B%20type:%20%22neuron-search%22,%20id:%20%22neuron-search-1%22,%20options:%20%7B%22annotation-name%22:%20%22papers%22%7D%7D,%200.6)
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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.
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Above, my #introduction of interests. Here, who I am, what I do: a neuroscientist at the #MRCLMB and University of Cambridge, UK, studying the neural circuit basis of behavior, originally in #Drosophila but now also in #cephalopods (#pygmysquid #Idiosepius), the lancelet #Amphioxus and other animals. Our main approach: whole brain #connectomics with #vEM (volume electron microscopy) as the basis for computational modeling to guide neuronal activity perturbation and monitoring experiments with #optogenetics and #electrophysiology (#ephys for short).
Once upon a time I founded the #ImageJ -based #TrakEM2 software for image registration and neuronal arbor reconstruction and annotation, which spurred founding the #FijiSc (https://fiji.sc) image processing software, and later the #CATMAID web-based software for #connectomics.
Always open to inquires from prospective students and postdocs, and collaborations.