ZEISS arivis Cloud: a cloud-based platform for deep learning model training and scalable bioimage analysis
Posted on: 29 October 2026
Preprint posted on 13 August 2026
Categories: bioinformatics, cell biology
Background
The field of microscopy and bioimage analysis has benefited substantially from the advances made in machine learning and artificial intelligence. Most bench researchers nowadays can conduct tasks such as manual annotation and segmentation without the need for expertise in imaging. However, model training requires computational skills to some extent which researchers looking to answer biological questions might not always possess. Moreover, analysing large numbers of images to obtain quantitative information remains challenging, as reproducibility must be maintained across image analysis pipelines, experiments and acquisition parameters.
In this preprint, the authors present ‘ZEISS arivis Cloud’ which is makes deep learning model training more accessible and facilitates the reproducible analysis of large image datasetsby combining browser-based model training with direct integration into acquisition and scaled analysis workflows, excluding the need for local computer infrastructure or programming expertise.
Key Features
Integration of user-friendly segmentation tools
The platform offers two types of AI-driven image segmentation models: 1) pixel-based semantic segmentation, where each pixel is assigned to user-defined classes to identify structures of interest, and 2) object-based instance segmentation, which allows detecting individual objects that might be touching or overlapping. The platform additionally integrates SAM (Segment Anything Model), an advanced AI model developed by Meta AI that can identify and segment any object in an image with a single click, thereby reducing the time required to label individual objects in an image.
Support of partial annotations
The segmentation pipelines include pre-trained model initialization to reduce the amount of labelled data needed during training. Indeed, annotation of every pixel isn’t required in every image since unlabelled regions are not treated as negative examples. The model is able to learn from incomplete labels without being penalized for correct predictions in unannotated areas.
Easily accessible platform
All the image analysis tasks can be performed on a web browser. The platform itself is a browser-based application hosted on Microsoft Azure and does not require local software installation or dedicated GPU hardware.
Application example
The authors used murine intestinal organoids to test out the arivis Cloud platform. They firstly employed a deep learning model trained in arivis Cloud to guide image acquisition which improved organoids detection and reduced the imaging time required for a multiwell organoid experiment while ensuring the collection of high-resolution data.
To perform the analysis, they developed a pipeline combining the Cellpose algorithm for nucleus segmentation with a custom model trained in arivis Cloud for lumen segmentation. The pipeline extracted morphological measurements including organoid volume, cell layer volume, lumen volume, and organoid roundness, as well as per-nucleus and per-cell intensity measurements for differentiation markers.
They validated the pipeline on a representative subset of 30 organoids per experimental condition and then applied it across 60 organoid datasets in a single batch run to ensure that results were not affected by analyst-to-analyst variation or incremental changes in processing parameters across the dataset.
Overall, the analysis identified significant differences in cell number and Aldolase B expression between control and Wnt-inhibited organoids, consistent with a role for Wnt signaling in organoid maturation.
Testing out the platform
I signed up for a trial account on www.arivis.cloud to perform segmentation on some images I’m used to analyse by manual tresholding. First, I imported my 2D images (18 images in total) which represent maximum intensity projection acquisitions of Drosophila fat body tissues stained for lipid droplets (LDs).
The built-in segmentation tool was easy to use. By clicking on the brush tool to select my regions of interest, I was able to detect LDs without heavy manual annotations. In my example, I only annotated a few droplets from 9 images to train the model. Once I was done, I submitted the images and waited until the training was completed.
After the training, I was able to see the annotated LDs that the model identified which represents a small subset of the LDs present in the images. Therefore, I would need to refine segmentation through additional training by increasing the number of objects and samples.
What I like about the platform and the preprint
I always like to stay up to date with the latest bioimage analysis tools. In addition to the bench work, image analysis has become an important part of my job as a researcher. I find it encouraging to see platforms like arivis now allowing one to perform model learning without the need to learn complex coding or computational equations. I’m not saying that learning these isn’t important or useful, but with the increasing incorporation of AI tools in our everyday work it’s good to see new initiatives aimed at helping researchers without a computational background perform image analysis. This is what arivis Cloud does so well by integrating deep learning segmentation models with an easy-to-use web platform. I appreciate how this not only reduces the time spent on analysis, but also ensures that the output is reproducible. The authors clearly highlight this in the preprint and detail how the workflow of the platform is aligned with FAIR data principles. Finally, I appreciated how the preprint authors gave credit to well-established bioimage analysis tools used to perform segmentation.
Questions for the authors
- You mention that researchers can begin training with as few as approximately 20 annotated objects. Is this number based on different biological samples images tested on the platform?
- There are artifacts sometimes in immunostaining that make the acquired images too noisy with a lot of background signal. Would you recommend users to pre-process the images to reduce this before uploading them to arivis?
One of my biggest concerns when performing analysis to do cell counting, for example, is the difference in the signal between groups of images. This is relevant when working with different mutations or genotypes. The signal can be either stronger or weaker in images acquired under the same microscope conditions (laser power, gain offset…). How does the model training on arivis take these differences into consideration?
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