COSEM

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COSEM

COSEM

@COSEMdata

Using machine learning and computer vision techniques to automatically identify and quantify all intracellular substructures & organelles in isotropic EM data

Virginia, USA انضم Kasım 2020
97 يتبع148 المتابعون
COSEM أُعيد تغريده
OpenOrganelle
OpenOrganelle@OpenOrganelle·
Some new updates on the site, including rearranging and cleaning up the dataset pages 🪄. Also, did you know that you can open (or view) any of the datasets directly in @FijiSc using either File/Import/N5 or Plugins/BigDataViewer/N5 viewer? Check it out!
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Aubrey Weigel
Aubrey Weigel@avweigel·
Come work with us at @HHMIJanelia with the new 🖌 Annotation & Analytics 📊 team tinyurl.com/5xxy2v95. This is a ✨ NEW ✨ multi-disciplinary team that will work with a variety of datasets (1/6)
Aubrey Weigel tweet media
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COSEM أُعيد تغريده
Sadaf Shadan
Sadaf Shadan@sshadan·
2 Papers @Nature on the use of enhanced FIB-SEM to image cells and tissues at up to 4nm per voxel. The authors' AI-based analysis pipeline, which they used for reconstruction of 35 organelle classes, is available via OpenOrganelle go.nature.com/3lhin4w go.nature.com/3oEsthG
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COSEM أُعيد تغريده
Aubrey Weigel
Aubrey Weigel@avweigel·
It’s official! Our manuscript is out today in @Nature. 🥳 Additions since our preprint include a comparison of generalists trained on all training data vs specialists trained on subsets (spoiler alert: generalists win!) and #moreGolgi! rdcu.be/cy1UJ (1/3)
Aubrey Weigel tweet mediaAubrey Weigel tweet media
Aubrey Weigel@avweigel

We are proud to share our work on segmenting up to 35 organelles & macromolecular structures in whole cell isotropic FIB-SEM at 4 & 8nm resolution biorxiv.org/content/10.110… 1/12

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COSEM أُعيد تغريده
OpenOrganelle
OpenOrganelle@OpenOrganelle·
We’ve also gotten a facelift! 💁‍♀️ Browse all the related data for these papers and more - AND - volume renderings are now available for most segmentations. Check it out on openorganelle.janelia.org !
Aubrey Weigel@avweigel

It’s official! Our manuscript is out today in @Nature. 🥳 Additions since our preprint include a comparison of generalists trained on all training data vs specialists trained on subsets (spoiler alert: generalists win!) and #moreGolgi! rdcu.be/cy1UJ (1/3)

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