Discover and read the best of Twitter Threads about #TrackMate
Most recents (5)
Tracking droplets fusion in microfluidic chamber with #TrackMate, a short thread.
Yesterday I got to discuss with Liridon Aliti (on forum.image.sc) about detecting droplet fusion events in movies like this one:
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Yesterday I got to discuss with Liridon Aliti (on forum.image.sc) about detecting droplet fusion events in movies like this one:
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We tried to do it in TrackMate, it was fun and I would like to share how we did it. It serves as a demo of the new TrackMate capabilities, but was done in <10min per movie, so it should not be considered a thorough analysis (we only tried one detector one tracker).
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The droplet are border objects, so using the excellent morphological segmentation in Fiji #MorphoLibJ by @david_legland and @IgnacioArganda seemed like a good idea. Plus the image quality is incredibly good given the fps (2k here)
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1/21
We’re proud to present a new Deep Learning approach for Content-Aware Frame Interpolation (CAFI). Ever wanted to increase the frame rate of your images using Deep Learning? CAFI might be the right tool for you.
It’s CAFI time!
github.com/mpriessner/CAFI
biorxiv.org/content/10.110…
We’re proud to present a new Deep Learning approach for Content-Aware Frame Interpolation (CAFI). Ever wanted to increase the frame rate of your images using Deep Learning? CAFI might be the right tool for you.
It’s CAFI time!
github.com/mpriessner/CAFI
biorxiv.org/content/10.110…
2/21
Delighted to share this work, a collaboration with @dgaboriau, @SheridanArlo, Tchern Lenn & Jonathan Chubb (not on Twitter), @manorlaboratory, @Vilar_lab and @LaineBioImaging
biorxiv.org/content/10.110…
Delighted to share this work, a collaboration with @dgaboriau, @SheridanArlo, Tchern Lenn & Jonathan Chubb (not on Twitter), @manorlaboratory, @Vilar_lab and @LaineBioImaging
biorxiv.org/content/10.110…
3/21
CAFI provides Deep Learning-based temporal super-resolution for fast bioimaging. It increases the frame rate of any microscope modality by interpolating an image in between two consecutive images via “intelligent” interpolation, providing 2x increase in temporal resolution.
CAFI provides Deep Learning-based temporal super-resolution for fast bioimaging. It increases the frame rate of any microscope modality by interpolating an image in between two consecutive images via “intelligent” interpolation, providing 2x increase in temporal resolution.
New preprint from the IAH facility in @institutpasteur with @guijacquemet lab , @DumenilLab and friends:
Features a new version of #TrackMate, that broadens a lot its versatility.
"Bringing TrackMate in the era of machine-learning and deep-learning"
biorxiv.org/content/10.110…
Features a new version of #TrackMate, that broadens a lot its versatility.
"Bringing TrackMate in the era of machine-learning and deep-learning"
biorxiv.org/content/10.110…
Link to threads that tries and detail the new stuff:
1/ new detectors and segmentation algorithms based on Deep-Learning and Machine-Learning.
1/ new detectors and segmentation algorithms based on Deep-Learning and Machine-Learning.
2/ Using TrackMate to segment 3D objects with a slice-by-slice approach.
A 2nd post about the new #TrackMate version:
Using TrackMate to segment 3D objects using a slice-by-slice approach.
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Using TrackMate to segment 3D objects using a slice-by-slice approach.
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Now that we have nice Deep-Learning based and Machine-Learning based segmentation algorithms that work especially well in 2D, we can use them to segment 3D objects with TrackMate.
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The idea is to tell TrackMate "this is not a 3D image but a 2D+T image that you will track".
For instance you could use StarDist on the individual 2D slice to segment sections of the object, then merging the multiple 2D contours in a single 3D object.
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For instance you could use StarDist on the individual 2D slice to segment sections of the object, then merging the multiple 2D contours in a single 3D object.
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New #TrackMate version out (v&7)
With major changes and improvements I describe below (probably a long thread).
It is the product of a great (and cool) collaboration with @guijacquemet lab (@JwPylvanainen), the @DumenilLab lab in @institutpasteur and the IAH facility
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With major changes and improvements I describe below (probably a long thread).
It is the product of a great (and cool) collaboration with @guijacquemet lab (@JwPylvanainen), the @DumenilLab lab in @institutpasteur and the IAH facility
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The new version is the product of an almost Codiv19-induced full rewrite of TrackMate. Our main goal was to allow integrate SOTA segmentation algorithms and tools such as #StarDist, @ilastik_team, #Weka, @MorphoLibJ and #cellpose in TrackMate,
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so that they can be used in a tracking pipeline and improve the tracking accuracy and results.
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