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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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Releasing #cellpose, a generalist algorithm for cellular segmentation. Try it now directly on the website cellpose.org, install the code package github.com/MouseLand/cell…, read the preprint biorxiv.org/content/10.110… or watch the talk .
We developed Cellpose as a generalist algorithm, because many small and big problems in biology require cell segmentation, and there just isn’t enough time to write a new pipeline for every type of data.
We trained Cellpose on a diverse set of 608 images, collected and segmented by us. See the t-SNE plot of image styles below.
