Adapt VAIDR
to your application

VAIDR offers very powerful image analysis for very little effort.

Whatever you can consistently see in your images, VAIDR can quantify. Often, even things you do not see. Due to the use of AI-algorithms, amazing analysis results can be achieved even on label-free images.

In contrast to other techniques, it does not take special skills or frustrating efforts to get there. VAIDR makes the use of AI in image analysis practical.

VAIDR can work with all cell types. Adherent, suspension, aggregates, organoids – you name it. The algorithm is not limited to any particular application. For any given set of images, VAIDR’s user-trainable AI automatically constructs the proper analysis algorithm to answer your research question.

The VAIDR microscope provides digital phase contrast and one fluorescence channel of your choice. The microscope automatically images any type of plate or cell culture dish using auto-focusing. The imaging workflow is highly automated – no need to tune any settings.

The VAIDR microscope makes high-contrast label-free images which are great for machine-learning. In many cases, the contrast and level of detail are better than in standard phase-contrast microscopes. The additional fluorescence channel extends the range of applications significantly.

The VAIDR microscope is seamlessly integrated into the platform. This means you do not need to manage your images anywhere else and then upload them, as is the case when using a third-party microscope.  

So, if you do not currently have an automated imager and the imaging modalities of the VAIDR microscope are sufficient for your use-case, it is a great option.

Yes, definitely. VAIDR has a simple upload interface where you can upload TIF images for entire plates including plate layout data. Since almost all microscope software packages have a TIF export option, you can use virtually any microscope as a data source for VAIDR.

For the segmentation workflow: 

It depends mostly on how variable your phenotypes are. In many cases, a surprisingly accurate segmentation can be trained by labeling a single image. Most of the time, segmentation models require less than 10 labeled images. Due to the smart labeling workflow, most of these images do not need to be labeled completely, but the user only needs to correct the mistakes that an earlier method version made, to train an improved version.

For the classification workflow: 

Since this has a built-in statistical validation feature, the data requirements depend on the level of validation you need. For a quick check whether phenotypes are distinguishable, images from a few replicate wells per condition are sufficient. To establish a routine classification workflow, images from several batches/timepoints etc. may be required.

This ranges from 1-2 hours for a small segmentation training to several hours for a large study with a comprehensive validation package.

Things to consider are: How do you intend to validate the trained method and against what type of variability do you need your method to be robust? In general, the training data should encompass all the variability that the trained method will encounter during its application.

So, for a one-off demonstration, within a fixed data set, there just need to be sufficient test data that were not used for training, to demonstrate that the method has learned something relevant and did not just memorize the training images. This principle applies generally: Always set some test data aside to check if the method generalized from the training.

On the other end of the spectrum, if the method is intended to analyze data that will be generated in the future, it is important to keep an eye on the performance and potentially add new training data to update the method. This may be necessary when the encountered phenotypes evolve over time.

A better answer to this question usually requires more specific knowledge about the application. The VAIDR team are always available to help with experimental design and best practices to make your project succeed.

VAIDR analyzes cells. To do this well, it combines several powerful capabilities into one package: 

• The AI is trained to the specific requirements of your application. This ensures that the AI will actually work on the myriad of diverse applications in the field. 
• To make this practical, VAIDR has an intuitive, smooth workflow. It is easy to learn, time-efficient & fun to use. 
• Seamless integration. From samples to the final results, you can do everything within the VAIDR platform. 

Because VAIDR uses user-trainable AI, the possibilities are endless. But here are some examples: Viability, cell count, substructures like nuclei, nucleoli, organelles, neurites. Co-culture analysis, unbiased scoring of treatment effects, detection of genetic defects, quantification of differentiation phenotypes, ...

Have a couple of nice reads with our application notes or sit back and watch some videos on our YouTube channel.

There are three main steps: Data input, training, and analysis. 

1. For data input, you either upload images from your existing microscope, or you use the VAIDR microscope, which is directly integrated with the platform. 
2. For training, you either use our labeling editor in the segmentation workflow to teach VAIDR what you want to detect and quantify. In the classification workflow, you simply assign entire groups of wells to your classes of interest and train a validated classifier. 
3. For analysis, you simply apply the trained methods to lots of images. You can either evaluate the quantitative results inside the VAIDR platform or export them.