iiinova — where your digital self keeps evolving

PPS. Some really awesome stuff from our group coming very soon! 😉 (7/7)

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PS. I presented our new automatic segmentation framework integrated in μSAM (selected as a Spotlight Presentation) -- check out the paper (openreview.net/forum?id=xFO...) and our software (github.com/computationa...)! (6/7)

It's great to interact with such a rich community at MIDL Conference around and beyond the academic discussions (and ofc meeting old friends and making new ones). Looking forward to #MIDL2027 in Porto already! 🥳 (5/7)

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Gossip of the town -- continuing tradition of Karaoke evenings and Typhoon! 🌀 (4/7)

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Some feel good moments -- quite a few efforts using our μSAM tool already 😁 (3/7)

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Highlights for me -- active research with Vision Foundation Models and Radiology Report Analysis / Generation, noticable activity in Spatial Transcriptomics, and tons of awesome work in the intersection of Scene Reconstruction and WSI analysis! (2/7)

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

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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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 @handle.invalid · 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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There are exciting opportunities and foreseeable clinical applications of vision foundation models for biomedical image analysis! The MIDL Community did seem excited, and so are we! (some amazing follow-up applications coming soon, keep an eye open 😉)

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And both of our efforts and these beautiful presentations at #MIDL2025 have been successful due to immense efforts from our amazing lab members @caroteu.bsky.social and Titus Griebel. And of-course @cppape.bsky.social being the absolute best PI, as always (I can't say this enough tbh)!

And sticking to our @cppape.bsky.social lab's theme of rocking in the open-source world, all the models and code are completely open to use and easy to access. You can take @midl-conference.bsky.social's word on this! 😉 PathoSAM: github.com/computationa... Late PEFT: github.com/computationa...

Parameter Efficient Fine-Tuning of Segment Anything Model for Biomedical Imaging

Next, we introduce a novel finetuning paradigm for foundation models "Late PEFT", inspired by our striking observations of PEFT's memory requirements, questioning: "Are PEFT methods Resource Efficient?" We have an answer: PEFT "can be resource efficient"! Check out more at: doi.org/10.48550/arX...

doi.org

Segmentation is an important analysis task for biomedical images, enabling the study of individual organelles, cells or organs. Deep learning has massively improved segmentation methods, but challenge...

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To begin with, here's "PathoSAM", our foundation model for nucleus segmentation in histopathology, presented as an Oral! (and my very first one). If you want a SOTA model for interactive and automatic segmentation for your histopathology images, go check out the paper now doi.org/10.48550/arX...