Image Analysis Service

The Imaging and Optics Facility provides several high-end Image Analysis Workstations equipped with state-of-the-art Image Analysis Software. To best coordinate the utilization of these resources, including data storage & data transfer, please make yourself familiar with our Booking policy & Data storage policy.

The overall aim is to set up an optimized Acquisition -2- Image Analysis pipeline:


(see IOF equipment)

Image Processing

(stitching, deconvolution)

Image Analysis

(segmentation, tracking)

Data Presentation

(visualization and plotting)

Image Analysis Infrastructure Services

Image analysis services rely on a good data-infrastructure ensure safe and reliable data-transfer and data-storage (Data storage policy). Besides mediating in the data infrastructure requirements with our IT department, the Imaging and Optics Facility also provide assistance to establish analysis workflow and/or develop scripts. We aim to place workflows protocols and scripts in a repository (GitLAB), as well as that some of these scripts are available on our website. More information about our Image Analysis Resources can be found on the Image Analysis Resources page.We strive to keep our image analysis services at the forefront of ongoing development, by:

  • Image Analysis Workstation Room: i01.O3.52a & i01.O3.52b
  • Data-infrastructure: updating our image analysis equipment and software
  • Training: instruction training to use image analysis software / applications by IOF staff
  • Assistance: single sessions with IOF staff support for specific applications /modalities
  • Project: collaborative support from IOF to establish image processing / analysis routines
  • Test & demonstration of new image analysis software applications
  • Scratch: a centralized storage location for image processing and advanced analysis tasks
  • Gitlab: a repository for analysis routines, workflow & scripts, available for the ISTA community, and beyond…

Image Analysis Rooms: I01.O3.52-a/b

IOF has 2 image analysis rooms i01.O3.52a & i01.O3.52b equipped with multiple ‘image analysis terminals’ that have 2 screens to optimize your image analysis work, and provide an environment that allow you to in focus on image processing and analysis tasks.

Scratch: a central data processing drive

Our workstation are all connected to a network drive named ‘scratch‘ via an 10 Gb network! This allows users to process data directly on scratch. Thus copying to local workstation hard drives is not needed anymore, thereby saving you a lot of time. In addition, scratch is connected to all workstations. You do not have to book any specific workstation anymore because your data is stored on that system. You do need to check if the workstation has the specification and software that you need to perform your analysis. The scratch drive is only mounted when users log on with their own user-profile -> do not forget to log-off after your session.

You can also mount scratch ‘manually’: just copy the following address into the address bar of the file explorer and provide your IST\username credentials when prompted: \\\scratch-bioimaging\

  • Each research-group has his own folder that is only accessible by the group members
  • Data that has not been re-saved for longer than 30 days will automatically be deleted


The Imaging and Optics Facility Gitlab is a repository where we share scripts and image analysis tools that seem useful for multiple users.

Data transfer

Image Analysis Resources

In addition to the available infrastructure, we offer project-based custom-tailored image analysis tasks (scripts/macro’s) in different platforms: FIJI, Matlab and Imaris (workflow).

  • Establishment of new image analysis routines using the image analysis programs available.
  • Automation of your image analysis workflow and batch processes
  • Customized image analysis script and/or macro’s
  • Machine Learning based image analysis pipe-lines (Ilastik, CARE, N2V, SLEAP, CellPose,…)

 Please consider that many of our FIJI scripts and macros rely on additional plug-ins (how to). We advice you to add the following plug-ins to your FIJI installation: Stower-lab, Cookbook, BAR, Big-stitcher, HDF5

IOF FIJI macros

  • Screen-capture macro:
    allows you to follow your experiment remotely. A screenshot made of the main computer screen and stored on a location of your choice. When you store the image on your file-server, you can access it remotely and follow your experiment. Contact us in case you need assistance
  • Full-width-half-maximum of 2D spots:
    This jython-fiji macro computes the FWHM on bright spots on dark background. For each spot the horizontal, vertical and two diagonal line intensity profiles are extracted and a general Gaussian function is fit, from which several parameters are extracted and exported to an Excel readable file.
  • Root tracker concatenator:
    This macro is part of the mini-macros repository. It concatenates the files generated by the root tracker based on zen blue. It finds the files with the same extension, stacks the corresponding scenes and sorts dimensions (slices and time frames) properly. File and folder names must not have spaces. Use the concatenateTrackedImages.ijm file. More details in concatenateTrackedImages.txt.
  • Slice scanner file handler:
    This macro is part of the mini-macros repository. It generates tiff series from slide scanner vsi files, generating upright and inverted views, re-assigning LUTs according to a selection and centering all the scenes in a canvas of the same size to stack images together. Use the SliceScanner.ijm file. Instructions in SliceScanner.txt.
  • Batch split Slice scanner :
    This macro is part of the mini-macros repository. It splits vsi series into separate tiff series. The images are placed and centered in a canvas of the same size so images can be stacked together. Use the batchSlitSlideScanner.ijm file. More details in batchSplitSlideScanner.txt.
  • Flatfield correction:
    This macro is part of the mini-macros repository. Small script to correct illumination profiles for confocal images. Use the flatfieldCorrection.ijm file. Instructions in flatfieldCorrection.txt.
  • General concatenator:
    This macro is part of the mini-macros repository. It sorts mixed file series, grouping files according name filters. Use concatenator.ijm. More details in concatenator.txt.
  • Concatenate OME Tiff stacks:
    This macro is part of the mini-macros repository. Builds hyperstacks based on the parsed OME Tiff file names. Script: ConcatenateOmeTiffStacks.ijm. More details: ConcatenateOmeTiffStacks.txt.
  • Temporal gradient
    Compute (smoothed) temporal gradient of 3D images (Fiji)

IOF Python Scripts

  • Convert OMEtiff:
    Images from the OpenSpim microscope write multi-file OMEtiff images, which contain redundant meta-data. Especially for big experiments this can lead to long waiting times when loading these images with Fiji. This script removes the OMEtiff metadata and re-saves it as Fiji tiff hyper stack.
  • Convert_roottracker:
    Image-sequences from the tracker are converted into concatenated time sequences per position. download → Open Fiji → Choose Plugins>Install Plugins
  • Undrift
    Non-linear registration method to stabilize local movement in 2D, multi-channel movies.
  • Temporal color-code movies
    Fast(er) and less memory expensive computation of temporal color codes of 2D movies

IOF Microscopy Automation Macros

  • Multiblock Made Easy: Workaround for crashing Experiment Designer sessions. This Macro should be installed already in the Zeiss LSM 800 confocal microscopes. You can see the instructions in the PDF file

IOF Matlab Scripts

We are converting our scripting efforts to open source programs such as FIJI/ImageJ and/or Python based applications. In case a matlab-script is required, we will consider upon request.

Machine Learning-based Image Analysis Services @ IOF

We provide machine learning approaches, such as:

  • Ilastik: interactive image classification, segmentation and analysis
  • CARE: Content Aware image REstoration
  • Noise2Void (N2V): powerful, self-supervised, deep-learning algorithm for image denoising
  • CellPosse: segmentation algorithm
  • SLEAP: framework for estimating positions of animal body parts

We developed a a simple Jupyter based graphical user interface, called CARE-less: a simple Jupyter based graphical user interface for CARE and Noise2Void.

Additional information about Care-less and the available ML applications are listed on our Machine Learning Application page

Image Analysis Community Resources

Image analysis is driven by a strong community, aiming to contribute and share findings and resources. please see below:

Image Analysis Reading Tips & Resources

Recommended Image Analysis Community Courses & Workshops

Image Analysis Community Network / Forums