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#napari

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sirhc

Started work on maybe the first real enhancement to #yt_napari that might be useful to the #napari community at large by adding the ability to export image layers from napari to a yt dataset. Currently putting to it to use by building a little widget for yt's PhasePlot (a 2D binned statistic with optional weighting) that lets you select from available layers and get back a nice plot. Still lots of UI improvements to do... but it's a start!

Screen shot of the napari GUI that shows a 3d multichannel image from napari's sample datasets (something related to cells... I don't know, I'm not a biologist...). The middle of the image shows a 3d views of cell structures. The right side of the image shows a new PhasePlot widget that includes drop down menus for selecting the x, y and z arguments for a 2D binned and weighted histogram along with a resulting 2D weighted histogram below.
#python#foss#opensource
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Kyle Harrington

it took longer than i care to admit to revisit Talley Lambert's revised pattern for the Pulser's library: github.com/kephale/pulser/blob

but i finally have a significant need to use this for a bespoke annotation tool. such an easy pattern to extend.

GitHubpulser/example.py at main · kephale/pulserA library of psygnal generators. Contribute to kephale/pulser development by creating an account on GitHub.
#python#midi#hci
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Moritz Negwer

Interesting new #preprint about #tissueclearing and #lightsheet #microscopy in mouse ovaries. Complete with #napari-based deep-learning pipeline and detailed build instructions for 3D-printed sample chambers for solvent-cleared samples, so they can be viewed under a #confocal!

OoCount: A machine-learning based approach to mouse ovarian follicle counting and classification
Folts et al., preprint at biorxiv 2024
biorxiv.org/content/10.1101/20

Fig. 3: Mounting and preparing adult ovaries for imaging. 1. Print a coverslip'n'slide and gather materials for mounting the samples. Note the fillinf inlets on the coverlip'n'slide indicated with the blue arrows. 2. Apply super glue around the edges of a coverlslip and attach it to the the coverslip'n'slide. 3. Load syringe with sealant for more precise control over application. 4. Using the syringe, create a chamber using sealant by lining the edges of the coverslip. 5. Arrange the samples on the coverslip. 6. Apply a coverslip to the top of the sealant, creating a chamber. 7. Gently push on the top coverslip until it touches the samples. 8. Using a pipet, fill the chamber with ECi. 9. Plug the filling inlets with sealant. 10. Allow to cure for 1h before imaging.
Public
Stephen Royle

Preprint describing Nellie, a napari plugin for automated organelle segmentation. Results look impressive!

arxiv.org/abs/2403.13214

repo (with video): github.com/aelefebv/nellie

arXiv logo
arXiv.orgNellie: Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopyThe analysis of dynamic organelles remains a formidable challenge, though key to understanding biological processes. We introduce Nellie, an automated and unbiased user-friendly pipeline for segmentation, tracking, and feature extraction of diverse intracellular structures. Nellie adapts to image metadata, eliminating user input. Nellie's preprocessing pipeline enhances structural contrast on multiple intracellular scales allowing for robust hierarchical segmentation of sub-organellar regions. Internal motion capture markers are generated and tracked via a radius-adaptive pattern matching scheme, and used as guides for sub-voxel flow interpolation. Nellie extracts a plethora of features at multiple hierarchical levels for deep and customizable analysis. Nellie features a point-and-click Napari-based GUI that allows for code-free operation and visualization, while its modular open-source codebase invites extension by experienced users. We demonstrate Nellie's wide variety of use cases with three examples: unmixing multiple organelles from a single channel using feature-based classification, training an unsupervised graph autoencoder on mitochondrial multi-mesh graphs to quantify latent space embedding changes following ionomycin treatment, and performing in-depth characterization and comparison of endoplasmic reticulum networks across different cell types and temporal frames.
#CellBiology#microscopy#napari
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Mikkel Roald-Arbøl

The #neuroscience Mastodon hive-mind worked wonderfully earlier - let’s try again!

In #napari, I have loaded a video using the `napari-video` plugin. I would like to get some simple image statistics for all frames in the video - does anyone know how to? I have looked through the various plug-ins, but none seem obvious… but it’s so simple (no segmentation, nothing complicated), I feel like I must be missing something!

Public
sirhc

Having a ton of fun utilizing other plugins with yt-napari. This video shows some interactive plotting with napari-clusters-plotter after I used it to run a kmeans classification of individual pixels across 3 image layers (2D samples through yt's IsolatedGalaxy test data set).

Full description will be up on youtube soon (final installment of the yt-napari tutorial series!).

#yt-project #yt_astro #DataVisualization #napari @napari

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