Mastodawn

Tried the same with a more realistic 3D stack from the #ImageJ sample library.#Cellpose runs fast and segments very well out of the box.#CellSeg3D takes considerably longer and seems to segment decently, but I couldn’t get a proper instance #segmentation in the post-processing step (which is recommended as part of its workflow). However, #CellSeg3D looks very promising — just needs some more time and parameter exploration, I guess.

I’d recommend giving it a try 👌

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Fabrizio Musacchio Jul 13

Tested #CellSeg3D and #Cellpose on their example c5image dataset. Both segmentations look reasonable out-of-the-box, without any deep parameter tuning. With some extra effort, one could likely push either further I guess. Overall, both tools perform quite well on this small sample data set.

Fabrizio Musacchio Jul 11

✍️ New in #eLife: #CellSeg3D introduces #WNet3D, a self-supervised 3D #segmentation method for #microscopy data — no labels needed. Claims to outperform #Cellpose/#StarDist on 4 datasets. Includes #opensource plugin (#Napari) + full 3D annotated #cortex dataset. Will test it later.

🌍 https://elifesciences.org/articles/99848

#DeepLearning #Neuroscience

CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy

Self-supervised deep learning models can accurately perform 3D segmentation of cell nuclei in complex biological tissues, enabling scalable analysis in settings with limited or no ground truth annotations.

eLife

CSBJ May 27

🤖 How resilient are deep learning models to real-world imperfections in microscopy?

🔗 Practical guidelines for cell segmentation models under optical aberrations in microscopy. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2024.09.002

📚 CSBJ Quantum Biology and Biophotonics: https://www.csbj.org/qbio

#CellSegmentation #Microscopy #AIinBiology #OpticalAberrations #DeepLearning #BiomedicalAI #Cellpose #ComputationalBiology #PLCM

AI4Life Apr 26, 2024

After a day and a half of #ImageAnalysis in the cloud with #dask by the IDR team, Damian Dalle Nogare takes over a practical session to apply several #AI models for cell segmentation using #Cellpose

Carsen Stringer (she/her)Apr 8, 2024

Despite all their hype in AI, we found that Transformers do not outperform #Cellpose for cellular segmentation tasks: https://www.biorxiv.org/content/10.1101/2024.04.06.587952v1

#bioinformatics #biology #machinelearning

Carsen Stringer (she/her)Feb 14, 2024

#Cellpose 3! Not all images are perfect. Restore your images with Cellpose3 to get better segmentations, w/ @marius10p https://www.biorxiv.org/content/10.1101/2024.02.10.579780v1

(click to play gif)

Carsen Stringer (she/her)Nov 29, 2023

AI under the microscope: the algorithms powering the search for cells: https://go.nature.com/3SVdFur. Nice article by Michael Eisenstein about cellular segmentation, with a shout out to #cellpose :) #biology #deeplearning

AI under the microscope: the algorithms powering the search for cells

Deep learning is driving the rapid evolution of algorithms that can automatically find and trace cells in a wide range of microscopy experiments.

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Carsen Stringer (she/her)Jul 27, 2023

@ogi thanks, I did not realize this before, that's great. and yes we use #pyqtgraph in a bunch of projects, love how fast and flexible it is! #cellpose is our most popular software tool that uses it: https://github.com/MouseLand/cellpose