Sebi
@sebi06.bsky.social
130 followers 200 following 45 posts
I am a Software Architect for AI Solutions, Python Fan, Microscopist Image Analyst and really like Skiing. Science is like magic but real.
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sebi06.bsky.social
You need to add our ZEN API interface to that site?

github.com/zeiss-micros...
sebi06.bsky.social
Kind off .... but by rahter by showing off out ZEN API at an AiryScan Demo.
sebi06.bsky.social
Guess what i am just doing ...?
Reposted by Sebi
cellmorphosero.bsky.social
Americans: tell your friends who are worried about Tylenol that there's a safe alternative available in the UK.
For a modest fee, I will send them Paracetamol, which is NHS-approved for pregnancy and for children.
You can even get it combined with decongestant to make a tasty hot lemon drink.
sebi06.bsky.social
The first day is open for all (while day 2 and 3 will be hands-on with a limited number of people)

- Learn about concepts and tools available
- AI tools within ZEISS ecosystem
- Guided Acquisition without coding
- ZEN Scripting
- ZEN API - Control ZEN via Python

and more!
sebi06.bsky.social
Hi Fans of Smart Microscopy,

Are you ready to automate your next large scale microscopy experiment and work smarter, not harder?

Join us for the Smart Microscopy workshop organized by EPFL Bio-Imaging Platform (Lausanne) and @zeiss-microscopy.bsky.social

www.zeiss.ch/mikroskopie/...
sebi06.bsky.social
I saw this in my vaccation in a cafe in France.
As a person creating AI software tools I hope that is not true but I am not so sure anymore ...
sebi06.bsky.social
And now the real challenge starts - the data analysis :-)
Reposted by Sebi
will-j-moore.bsky.social
From #ome-ngff-validator you can open open #OME-Zarr images in a bunch of viewers, and neuroglancer's rendering of multi-channel images just got a lot nicer: ome.github.io/ome-ngff-val... - See github.com/google/neuro...
Reposted by Sebi
olivia.science
Finally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia:
doi.org/10.5281/zeno...

We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical
thinking, expertise, academic freedom, & scientific integrity.
1/n
Abstract: Under the banner of progress, products have been uncritically adopted or
even imposed on users — in past centuries with tobacco and combustion engines, and in
the 21st with social media. For these collective blunders, we now regret our involvement or
apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we
are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not
considered a valid position to reject AI technologies in our teaching and research. This
is why in June 2025, we co-authored an Open Letter calling on our employers to reverse
and rethink their stance on uncritically adopting AI technologies. In this position piece,
we expound on why universities must take their role seriously toa) counter the technology
industry’s marketing, hype, and harm; and to b) safeguard higher education, critical
thinking, expertise, academic freedom, and scientific integrity. We include pointers to
relevant work to further inform our colleagues. Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI
(black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are
in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative adversarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are
both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and
Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf.
Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al.
2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA). Table 1. Below some of the typical terminological disarray is untangled. Importantly, none of these terms
are orthogonal nor do they exclusively pick out the types of products we may wish to critique or proscribe. Protecting the Ecosystem of Human Knowledge: Five Principles
sebi06.bsky.social
Ah OK. Slow means it takes too much time for a single DF3 run?
sebi06.bsky.social
What exactly is not convincing and should be improved? And did you try the Microscopy Copilot?
sebi06.bsky.social
Another topic is that Smart Microscopy often requires dedicated (even low level) hardware control. Therefore making all those solutions "interoperable" across imaging system will be really, really, really hard ...
sebi06.bsky.social
I think one reason for the fragmentation is that it so much fun to create "your own smart microscopy solutions" (I know what I am talking about 😜). This is also the reason why existing commercial solutions are often not used ...

Plus the publishing issue that new is "cooler" than "re-use".
sebi06.bsky.social
I finally managed to upload the first version of the napari-czitools plugin (still in alpha - expect issues...)

- open complete CZI images or subsets
- read CZI metadata

pypi.org/project/napa...

or

github.com/sebi06/napar...
sebi06.bsky.social
Available Tools:

- Guided Acquisition incl. AI Segmentation
- Experiment Feedback
- Automated Photomanipulation
- ZEN Internal Scripting Interface
- ZEN API: Control ZEN "from the outside" using Python & more incl. direct access to Pixel Stream

Maybe they should be mentioned there as well 😜?
sebi06.bsky.social
As you might know I am big fan of smart microscopy because I worked on that topic in academia and also in several industry position (still do).

Therefore I have to point out that @zeiss-microscopy.bsky.social offers several tools that are not mentioned as "Industry Implementations".
sebi06.bsky.social
Are the slides available?
sebi06.bsky.social
But in general this could work with some tricks.
sebi06.bsky.social
czitools or pylibczirw or bioio-czi already allow to read CZI image files in parts.

So there is no need to get Napari involved?
sebi06.bsky.social
AFAIK Napari does not directly run inside a notebook.

But the python libraries used in that plugin can be used.

pypi.org/project/czit...
Client Challenge
pypi.org
sebi06.bsky.social
Here AI denoise can help to "improve" the image quality for follow up workflows like labeling or when applied online for navigation etc.