Machine Learning

With Machine Learning in arivis Vision4D the segmentation of multi-channel images becomes an easy task using the scientists expertise to mark and classify structures of interest without the need to be an image analysis expert:

  • Easy to use, guided workflow specifically for users with little knowledge in image analysis
  • Definition of expected results by labelling a few lines in the image
  • Creation of robust and reliable analysis results
  • Less time to results by re-use of trainings on other and multiple data sets
  • Fully integrated in the arivis Vision4D analysis module
  • Direct fast feedback with previews of results and probability maps
  • Much faster and more reproducible compared to manual segmentation
  • Also for experts worth a try when other algorithms fail or are very tedious


Our new UI and the smooth workflow integration guides the customer in a few steps through the process:







  1. Train: Use specific user knowledge to recognize and classify structures of interest
  2. Save training: Save or further improve your training by adding more labels any time
  3. Apply Training for segmentation of one or several image sets: Start segmentation with a single click or use the Batch Analysis Module to process hundreds of data sets at once


    Modalities and File Formats

    arivis Machine Learning works for several different multi-dimensional images from many modalities in microscopy:

    • Fluorescence Microscopy
    • Superresolution Microscopy
    • Transmission Light or Label Free Microscopy
    • Confocal Microscopy
    • Light Sheet Microscopy
    • Electron Microscopy (2D and 3D)
    • X-ray Microscopy
    • CT and MRT images

    All image formats supported by arivis Vision4D will also be compatible with Machine Learning. CZI, TIFF, JPG, PNG, TXM and all Bio-Formats compatible images.


    Full integration in arivis Vision4D

    Machine Learning in arivis Vision4D is fully integrated solution which allows to combine the segmentation results with any other functionality of the pipeline.
    These include:

    In addition you can also use the probability map as a basis for a subsequent segmentation in the arivis Analysis Pipeline.


    Why Machine Learning?

    A conventional algorithm is designed by a specialist to answer exactly one question. In contrast, a Machine Learning algorithm can be adapted to a wide variety of questions, simply by training it. The Machine Learning algorithm “learns” patterns and adapts itself.
    Often the user has some very specific knowledge to recognize and identify the structures of interest, but little knowledge about algorithms and image analysis.

    Machine Learning allows to use this expert knowledge of the user by drawing and classifying some samples of structures of interest into the image. The subsequent automatic training uses this information to automatically create the algorithm to be used to find these structures all over this and other images.


    • Usages of specific knowledge to identify the structures of interest
    • No need to invest time in sophisticated image analysis for users
    • Less investment in training users in image analysis for imaging facilities
    • Fast and easy to use offering more time to work on scientifically relevant questions