iiinova — where your digital self keeps evolving

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EMBL @embl.org · 07/09/2026

What is the best time to start a PhD? No time like the present. After years as a software developer, Anna Foix joined our International PhD Programme and now uses deep learning to decode biological insights from shape in microscopy images. Anna’s story 👇 www.embl.org/news/people-...

embl.org

We are EMBL: Anna Foix on doing a PhD later in life | EMBL

EMBL PhD student Anna Foix talks about the challenges and satisfaction of doing a PhD later than her peers.

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napari @napari.org · 26/08/2026

🚨🚨🚨 napari 0.9.0 is out! 🚨🚨🚨 This is a huge release for us and for our community, with over 30 people contributing new features, improvements, and bug fixes in the 6 weeks since 0.8.0! Release notes here: napari.org/stable/relea... and some highlights in the thread below! 👇

napari.org

napari 0.9.0

Tue, Aug 25, 2026 We’re happy to announce the release of napari 0.9.0! napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing l...

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Anwai Archit @anwaiarchit.bsky.social · 17/08/2026

📣 Deadline extended! The submission deadline for the MIDL Young Researcher Showcase 2026 has been extended to Friday, 21 August at 23:59 AoE! 🎉 www.midl.io/yr-showcase26

midl.io

MIDL Young Researcher Showcase 2026 - MIDL

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Constantin Pape @cppape.bsky.social · 16/07/2026

Segmenting individual cells in microscopy is much easier these days thanks to foundation models. Can we use these models for other tasks, such as cell classification? We investigate in our latest work, finding big improvements for object and pixel classification compared to classical approaches.

Evaluation of different methods for pixel classification (top) and object classification (bottom) on the LIVECell dataset. Dark green bars show the F1-Score, which measures the classification / segmentation quality (higher is better), light green bars show the runtimes. Five different settings are compared for each task: Ilastik features + random forest (RF), microSAM embeddings + RF, SAM2 embeddings + RF, and uSAM, SAM2 + attentive probing (DeAP and ObAP). microSAM feature perform best for RF based methods, attentive probing outperforms RF based approaches, but at a much higher runtime.

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Anwai Archit @anwaiarchit.bsky.social · 12/07/2026

Hi all, Felt like a good opportunity to share an update after a long time! 🥳 #MIDL2026 just got wrapped up, with another exciting conference in Taiwan! (1/7)

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MPI for Multidisciplinary Sciences @mpi-nat.bsky.social · 09/07/2026

AI meets cutting-edge microscopy: Welcome Constantin Pape, head of our new Machine Intelligence in the Life Sciences group! 👋 🔬 His team develops AI methods to analyze high-resolution microscopy & cryo-EM data to better understand the function of proteins and protein complexes inside cells. (1/3)

Portrait picture of Contantin Pape wearing a blue tshirt leaning against a sign of MPI-NAT.

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Constantin Pape @cppape.bsky.social · 09/07/2026

Sharing a big update: I started a group at the MPINAT in Göttingen! We will develop AI for analyzing how proteins interact in the cellular environment based on cutting edge imaging. This appointment is in parallel to the university, where I will retain my current group.

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Josh Moore @joshmoore.bsky.social · 03/07/2026

Johannes joined @gerbi-gmb.de with a clear first major task: help lead the completion of #NGFF RFC-5. Today, just before heading off on a well-earned holiday and just shy of his one-year anniversary, 0.6 has a release candidate. Kudos, @jo-soltwedel.bsky.social! 🍻🏝️🚀

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Constantin Pape @cppape.bsky.social · 23/06/2026

How can we use foundation models such as (micro)SAM to improve electron microscopy segmentation? Check out our new preprint where we found substantial improvements for nucleus, mito, and neurite-segmentation based on initialization and semi-supervised learning with foundation models.

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Chris Schmidt @schmchris.bsky.social · 16/06/2026

@jo-soltwedel.bsky.social presenting about how important FAIR data management is for efficiently leveraging AI in a trustworthy manner. Small differences in underlying data annotation and metadata can effect AI-based analysis output a lot. #ELMI2026 @gerbi-gmb.de #OME-Zarr

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Ali Shaib @alishaib.bsky.social · 01/06/2026

Hello #world, meet 1,000× Expansion Microscopy. A small gel would grow to the size of an Olympic swimming pool, while amino-acid-scale distances become visible with ordinary light microscopy. Led by Helena Hu from @eboyden3.bsky.social's lab, in collab with us. Story: www.biorxiv.org/content/10.6...

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Anna Foix @afoix.bsky.social · 26/05/2026

🔬 One more year! BioImage Computing has been accepted to @eccv.bsky.social 🎉 Show us your work at the intersection of computer vision, ML & biology! Check out our speaker lineup: @arratemunoz.bsky.social, @jakobtroidl.bsky.social & Juliette Griffié 👏 ⏳ Deadline: 13 July ℹ️ www.bioimagecomputing.com

bioimagecomputing.com

BioImage Computing

a truly interdisciplinary workshop

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Anwai Archit @anwaiarchit.bsky.social · 19/05/2026

At MIDL YRB, we love organizing cool online events that bring young minds in the bio(medical) imaging community together. This time, we’re excited to have @virginieuhlmann.bsky.social share her story! 🥳 Join us tomorrow, 20 May! www.midl.io/yr-storytime @midl-conference.bsky.social

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Constantin Pape @cppape.bsky.social · 05/03/2026

Looking for a PhD position at the intersection of AI, imaging, and gene therapy? Apply for this position in my lab: tinyurl.com/2a2v6tvx Part of sfb1690.uni-goettingen.de to study hearing, vision, and more. Plus, you can create pretty pictures as the one below :).

A cochlea imaged in light-sheet microscopy (right) with staining for spiral ganglion neurons (red) and inner hair cells (blue). You will develop AI-based methods to analyze these structures, for example via segmentation of the individual cells (right) that will support gene therapy development for hearing loss and a better overall understanding of the anatomy of hearing.

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Constantin Pape @cppape.bsky.social · 18/11/2025

Preprint alert! CochleaNet, our framework for analyzing light-sheet data of the cochlea. It consists of three networks to segment spiral ganglion neurons, inner hair cells, and to detect synapses. See rendering of a full cochlea in the image, find the preprint at doi.org/10.1101/2025....

Rendering of a full cochlea (left) with three stains (PV, VGlut3, CTBP2) shown in three different colors (red, blue, cyan). The whole cochlea is a spiral shaped structure, with spiral ganglion neurons (SGNs) in the inner helix, labeled by PV and inner hair cells (IHCs) in the outer helix, labeled by Vglur3. The figure also shows zoom ins. The right hand side shows segmentation results for SGNs, IHCs (represented by colored masks) and synapse detections (represented by colored dots).

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Anna Foix @afoix.bsky.social · 22/10/2025

Don’t miss Elena’s (from @ilastik-team.bsky.social lab) brilliant work @ #ICCV2025 with @anwaiarchit.bsky.social & @cppape.bsky.social poster 292 @ 11:15AM 🔬They tackle segmentation of massive 3D microscopy images & show how BatchRenorm removes tiling artifacts boosting transferability and clarity🌺

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Constantin Pape @cppape.bsky.social · 17/10/2025

Are you studying synapses in electron microscopy? Tired of annotating vesicles? We have the tool for you! SynapseNet implements automatic segmentation and analysis of vesicles and other synaptic structures and has now been published: www.molbiolcell.org/doi/full/10....

SynapseNet is a deep learning based software tool that automates the segmentation of vesicles, mitochondria, synaptic compartments, and the active zone. This is visualized in three panels. On the left, a section of an electron tomogram with a synaptic compartment densely filled with vesicles, which appear as round structures with dark boundary and light body in the image, is shwon. The top right panel shows the segmentation result of vesicles, visualized by masks with an individual color per vesicle and outlines for the segmented compartment (red) and active zone (blue). The bottom right panels shows a 3D rendering of the segmentation with vesicles shown as yellow spheres.

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ilastik-team.bsky.social @ilastik-team.bsky.social · 09/10/2025

Segment large images without tiling artifacts: sharing our work that should have been presented at ICCV in 2 weeks - the brilliant first author Elena can’t go because of visa issues. The paper: arxiv.org/abs/2503.19545 1/🧵

Large images have to be broken into tiles both for training and inference with neural networks. The tile predictions then need to be merged to produce the final volume prediction.

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Constantin Pape @cppape.bsky.social · 07/08/2025

Data is the key to AI advances in biology and "Still, when it comes to data, nothing compares to the real thing." An important editorial in Nature methods with a nice little shout out to microSAM: www.nature.com/articles/s41...

nature.com

Calling all data - Nature Methods

As life sciences research becomes enmeshed in the age of AI, real experimental data are more valuable than ever.

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Constantin Pape @cppape.bsky.social · 16/07/2025

Anwai represented the lab at MIDL very well! Read his thread for details on our two latest papers on foundation models for microscopy, histopathology and medical imaging.

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Anwai Archit @anwaiarchit.bsky.social · 16/07/2025

We presented our latest work on "PathoSAM" and "Late PEFT" last week at #MIDL2025 (Salt Lake City)! The community is growing and MIDL is becoming the venue-to-go for high quality research discussion!🧵

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Constantin Pape @cppape.bsky.social · 07/07/2025

Are you looking for an exciting position at the intersection of super-resolution microscopy and AI? Then check out the PhD and PostDoc position we offer for a joint project with the Group of Stephan Hell at MPI Göttingen. Please share with anyone interested, read on for links and details.

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Constantin Pape @cppape.bsky.social · 16/06/2025

We released version 1.6 of micro_sam: - Improvements for automatic tracking. - A new experimental mode for object classification. - **New versions of the LM and EM models** The models fix artifacts in automatic segmentation, see old vs. new prediction and better 3D segmentation results due to it.

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Constantin Pape @cppape.bsky.social · 28/03/2025

Announcing the new release v1.4.0 of microSAM. The main changes are: 1. Simplified installation on windows. 2. Preliminary support for automatic tracking. 3. Improved interface for model selection. Read on for a quick summary of the changes.

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Chris Schmidt @schmchris.bsky.social · 19/03/2025

@anwaiarchit.bsky.social & @cppape.bsky.social demonstrating the power of micro-sam, the napari plugin for the microscopy segment anything model, in their awesome workshop at the #TiM2025 conference in Münsingen. 🔬🦠💻

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Nature Methods @natmethods.nature.com · 13/03/2025

Our March issue is now live! 🥳 nature.com/nmeth/volume... The cover represents the process of cell and organelle segmentation by Segment Anything for Microscopy. Paper here: nature.com/articles/s41... Cover by Sebastian von Haaren.

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Anwai Archit @anwaiarchit.bsky.social · 13/03/2025

Another feather for Segment Anything for Microscopy. We made it to the cover for @naturemethods.bsky.social! And all thanks to our amazing @haarensv.bsky.social for this! <3

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Uni Göttingen @uni-goettingen.de · 26/02/2025

Automatische Zellanalyse mit #KI: Forschende trainierten eine bestehende, KI-basierte Software neu. Das Modell „Segment Anything for Microscopy“ kann Bilder von Geweben, Zellen und anderen Strukturen genau segmentieren: s.gwdg.de/HqkMz2; s.gwdg.de/iTejKW Forschungsteam mit bsky.app/profile/cppa...

Pflanzenzellen, die mit einem Fluoreszenzmikroskop aufgenommen und mit dem Modell automatisch segmentiert wurden. Die zugrunde liegenden Daten sind dreidimensional und das Bild zeigt eine Darstellung der segmentierten Zellen, die jeweils durch eine andere Farbe repräsentiert werden.

Foto: Nature Methods: 10.1038/s41592-024-02580-4

Segmentierung von Zellen in der Lichtmikroskopie mit μSAM. Das Bild zeigt, wie Zellen in der Phasenkontrastmikroskopie mit μSAM segmentiert werden können. Grüne Punkte und Kästchen zeigen die Benutzereingabe und farbige Masken die entsprechende Vorhersage des Modells.

Foto: Erstellt von Anwai Archit mit dem μSAM-Tool, verfügbar in Nature Methods: 10.1038/s41592-024-02580-4

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Anwai Archit @anwaiarchit.bsky.social · 24/02/2025

It was soooo much fun to brainstorm solutions with everyone, together!❤️ #EMBLDeepLearning

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Anwai Archit @anwaiarchit.bsky.social · 24/02/2025

Thank you @unigoettingen.bsky.social for the feature!😍 μsam got some super cool feature updates last week. Don't wait for the next release, go check us out now! github.com/computationa...

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Constantin Pape @cppape.bsky.social · 21/02/2025

Because we have seen these improvements and due to popular demand, cc @ritastrack.bsky.social @jianxuchen.bsky.social, we have decided to start a call for community data submission to further improve our models: computational-cell-analytics.github.io/micro-sam/mi... . Looking forward to any feedback

computational-cell-analytics.github.io

micro_sam API documentation

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Constantin Pape @cppape.bsky.social · 21/02/2025

Our next micro_sam release is here! We have a new model for light microscopy, that massively improves for automatic segmentation! See the qualitative and quantitative comparison in the images, v2 is our previous version, v3 is the new one.

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Rita Strack @ritastrack.bsky.social · 21/02/2025

Help improve MicroSAM!

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Josh Moore @joshmoore.bsky.social · 21/02/2025

Look at all of those #OMEZarrs! 🤩

Screenshare of a 3D rendering in neuroglancer.

Available under "View tomogram" on the following page: https://cryoetdataportal.czscience.com/runs/16497?deposition-id=10330

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Vojtech Dostal @vojtechdostal.bsky.social · 16/02/2025

I actually tested these two and also SamCell (recently on Biorxiv). Here's the quick and dirty results - might still be finetuned, and I might have not always picked the best settings.

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Constantin Pape @cppape.bsky.social · 21/02/2025

Our first deposition of synaptic vesicles segmentations is now in the Cryo ET Portal! We segmented vesicles in over 50 tomograms to enable analysis of membrane proteins and more. cryoetdataportal.czscience.com/depositions/...

Synaptic vesicles segmented with SynapseNet.

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German BioImaging @gerbi-gmb.de · 13/02/2025

🎤 Another speaker joins #TiM2025! We’re thrilled to announce @ritastrack.bsky.social as a new speaker, delivering an exciting talk on Wednesday. Huge thanks to her for joining us! Stay tuned for more updates on our TiM page: bit.ly/3BPN9fB

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Constantin Pape @cppape.bsky.social · 12/02/2025

After a long journey, Segment Anything for Microscopy is now published in Nature Methods! We significantly improve SAM for interactive and automatic segmentation in light and electron microscopy and build a user-friendly tool. www.nature.com/articles/s41...

Improvements in LM (top) and EM (bottom) of our micro-sam model (finetuned) compared to the default SAM model.

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Octavio Reyes-Matte @octavio-rm.bsky.social · 13/02/2025

Cannot really emphasize how amazing this tool is! For me, it checks many important boxes: - feels intuitive - powerful - customizable - flexible Many congratulations to @anwaiarchit.bsky.social , @cppape.bsky.social and the whole team! Looking forward to play with it and push its limits! 😁💻

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Anwai Archit @anwaiarchit.bsky.social · 12/02/2025

It is an honor to read these words from you, Rita. Read the beautiful summary by @ritastrack.bsky.social on μsam and CellPose3 in the 🧵!

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Constantin Pape @cppape.bsky.social · 12/02/2025

A lot happening for microscopy segmentation. Thanks for highlighting our paper and CellPose 3, Rita!

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Nature Methods @natmethods.nature.com · 12/02/2025

Segment Anything for Microscopy (μSAM) is based on Segment Anything, the vision transformer model for image segmentation, and offers generalist models for light and electron microscopy segmentation tasks. www.nature.com/articles/s41...

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Damian Dalle Nogare @damiandn.bsky.social · 12/02/2025

One of our most used tools these days in the facility. Really fantastic work!

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Anwai Archit @anwaiarchit.bsky.social · 12/02/2025

μsam is live in Nature Methods!🥳 Huge thanks to @cppape.bsky.social for being the absolute best PI, guiding me through every step in this journey, @ritastrack.bsky.social for amazing handling of our paper & all reviewers for helping us strengthen μsam. Go check our tool and paper right away!😉🧵

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Anwai Archit @anwaiarchit.bsky.social · 11/02/2025

PathoSAM is our attempt to bridge the gap of a unified foundation model for interactive & automatic nuclei segmentation in histopathology. Not only that, we show how it can be extended for a different segmentation task! Here's a trailer to so much more what PathoSAM offers!🥳🧵

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Anwai Archit @anwaiarchit.bsky.social · 05/02/2025

A question I get asked often: 1) How many images should I annotate for finetuning VFMs & 2) how much resources VFMs need for custom finetuning? Here's our attempt to answer them: Our novel efficient workflow enables you to harness the strengths of SAM with a few labeled images🧵

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Anwai Archit @anwaiarchit.bsky.social · 22/01/2025

MedicoSAM is here! 🥳 Our vision foundation model shows promising results for interactive segmentation, for both 2d & 3d medical images, across several tasks and imaging modalities! Don't take my word for this, check us out & try to "segment anything" on medical images! 😉🧵

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