Group leader @ Uni Goettingen applying Image Analysis and Deep Learning to large microscopy data in biology. Also @cppape.bsky.social
Goettingen
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This research will help us to build better tools for interactive microscopy analysis --- stay tuned for more! For more details: arxiv.org/abs/2603.19802
Thanks to Caro for driving this work and all other co-authors!
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We use embeddings from foundation models as features, either for a RF or different attentive probing variants. The features are averaged over masks for object classification. See the image for a method overview.
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Segmenting cells in microscopy is much easier these days thanks to foundation models. Can we use these models for other tasks, e.g. cell classification? See our latest work! We find big improvements for object and pixel classification compared to classical approaches.
ALT valuation 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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Want to learn about the latest trends and developments in bioimage analysis? Join us for the GLOBIAS workshop in Dortmund this November! I will be hosting a session on foundation models / AI. globias.org/activities/globi…
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How can we use foundation models such as (micro)SAM to improve electron microscopy segmentation? Check out our preprint! We found substantial improvements for nucleus, mito, and neurite-segmentation using initialization and semi-supervised learning with foundation models.
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Did it actually answer someone what a mitochondrion is 😱😱😱
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.
The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.
Access to all other Claude models is not affected.
We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.
Read our full statement: anthropic.com/news/fable-myt…
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Sorry to break the news, but the job of twitter hypeman was automated a few years ago already.
Today we all lost our jobs.....
Three Nature papers showing that scientists in the conventional sense are obsolete
At least read the first one.... the AI replaced all things that the scientist does ....
nature.com/articles/s41586-0…
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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/ that studies hearing, vision, and more.
Plus, you can create pretty pictures like the one below :).
ALT 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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This work was an excellent collaboration with the group of Tobias Moser, building on the cutting edge light-sheet microscopy of the Huisken lab.
Special thanks to Lennart, Aleyna, Elisabeth, and Martin! If you want to use CochleaNet, check out github.com/computational-cel…
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Preprint alert! CochleaNet, our framework for analyzing cochlear light-sheet data. It consists of three networks to segment spiral ganglion neurons, inner hair cells, and detect synapses. Rendering of a full cochlea below, find the preprint at doi.org/10.1101/2025.11.16.6…
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Looking for an opportunity to work on cutting super-resolution microscopy and AI? There's still time to apply to the two positions!
Our goal is to develop methods to understand the dynamics of biomolecules, using the latest developments in super-resolution microscopy and AI-based image analysis. Apply here:
uni-goettingen.de/de/699491.…
uni-goettingen.de/de/699489.…
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The science cuts are affecting incredibly important research. I am very sorry for Uri and the academic landscape in the US in general.
My lab’s 5-year NIH R01 grant, awarded to study gene therapy for hearing loss, was abruptly terminated. I want to share how this action has been incredibly harmful and disruptive, not just to my lab, but to the scientific process itself. 1/15
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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 RT, read on for links and details.
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For the full documentation of micro-sam head to computational-cell-analytics…. Also a big shoutout to @AnwaiArchit for the work on this release.
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3. The model selection now supports two additional models (Histopathology, Medical Imaging) and uses human readable names, see the screenshot below.
For more on these models check out: computational-cell-analytics…
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2. We added preliminary support for automatic tracking via trackastra, developed by @martweig. See the video for an example result and check out computational-cell-analytics… for details.
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1. Windows installation got easier:
- PyTorch is installed from conda-forge, eliminating the need for the pytorch channel.
- Fixing a problem with a C++ dependency.
Now, installation via `conda install -c conda-forge micro_sam` works on windows! Thanks to huge efforts from Mark!
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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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