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    Image-Analysis for Biologists

    This is the landing page for ZIDAS - SwitZerland's Image and Data Analysis School.

    So far, we have held six summer-schools (see below) and more are planned.

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    ZIDAS 2023 - Lausanne


    16th - 21st July 2023


    We are happy to announce that this year's edition will be in person in Lausanne. We are looking forward to an interesting and inspiring week of bio-image analysis.


    Best regards from the

    Organizing team  


  • About the School

    An intensive summer-course in image analysis for life-scientist.

    Using open-source tools and emphasising reproducibility.

    The What, Who, and Why

    What it is: A focused one-week school providing hands-on introductions to image processing and analysis, with priority given to biologically relevant examples. Students learn the fundamentals of image analysis, including basic macro programming in ImageJ/Fiji as well as other software solutions. By the end of the course all students have practical working knowledge of how to attack image analysis problems.


    Content: 1) Lectures on image processing interspersed with hands-on demonstrations and exercises. 2) Special topics: Machine learning, Super-resolution, stitching, high-volume data, deconvolution, co-localisation, tracking, image-ethics. 3) Project work: Students will work on real project data, typically their own. 4) Talks: Invited speakers, from Switzerlnad and beyond, presenting work that requires image analysis.


    Participants: The target group is from the life-sciences: PhD and master students, staff scientist, postdocs, and professors, from Switzerland and beyond. As funding allows, a few travel grants will be made available for external participants. Overall 25 students accepted to allow effective tutoring.


    Teachers: Didactically outstanding trainers from e.g. ETH Zurich, EPFL, Biozentrum and FMI Basel, University of Basel, EMBL Heidelberg, MPI-CBG Dresden, University College London, University of Edinburgh, IBMP-CNRS Strasbourg, Uni Carlos III Madrid, SciLifeLab Uppsala, and the wider NEUBIAS. Speakers will be invited from across Switzerland and Europe.


    Motivation & Background: There is a continued need for researchers in the life sciences to know how to handle their image data and this is typically not covered in their curricula. This school addresses this need.


    History (name change): 

    • 2017-2019: Zurich Image and Data Analysis School. 
    • 2020 onwards: SwitZerland's Image and Data Analysis School.
  • About the School

    This school provides a hands-on introduction to image processing and analysis, with an emphasis on biologically relevant examples

    Is this school for you?

    • Are you a life-science researcher with a pressing need to quantify your light-microscopy images?
    • Are you uncertain about how to: Best calculate co-localisation, do deconvolution, automate the counting of cells, track objects over time, handle massive amounts of image data, record your image-analysis work-flows in a reproducible manner?
    • If you answered yes to some of the above, then this school is for you!


    Digital images of high quality and quantity are now the norm in biomedical sciences. Ten to twenty years ago, when many current professors trained as students or post-docs, this was not yet the case as most microscopes were, at best, equipped with low-resolution digital cameras, celluloid-film (analogue) cameras, or no camera at all.


    This rapid change is rarely reflected in the curricula of life-science university departments and they offer few, if any, courses in image processing and analysis. Understandably, courses in image-analysis (computer-vision) in the computer-science departments tend to have different aims, work on different image-data, and pre-suppose literacy in at least one programming language, rendering them all but irrelevant for the life-scientist working in the laboratory.


    To bridge this gap, between what the life-scientist needs and what courses he/she is normally offered, we have created this school for image analysis. No former experience with programming is assumed, nor will much indeed be needed; only a strong desire to learn what can be done and how to do it is required.

    What you will learn

    You will learn the fundamentals of image analysis, including basic macro programming in ImageJ/Fiji as well as other software solutions.


    In the first part of the week we will also cover the process of image-formation as it pertains to image analysis: Resolution, correct exposure, point-spread functions, detector noise, Shannon's sampling theorem, and aliasing. All with a clear focus on application in the lab.


    In the second half of the week there will be a number of focused topics, building on what was learnt during the first days, possible topics are:

    • Deep Learning for image restoration and segmentation
    • Co-localisation, i.e., spatial correlation analysis and statistics
    • SMLM (Single-Molecule Localisation Microscopy) data analysis
    • De-convolution of microscopy images
    • Tracking of particles and cells in time-lapse recordings
    • Stitching and registration of stacks of large image data
    • Hands-on training with image-analysis software solutions such as  ilastik and CellProfiler

    Structure of the school

    You will be working actively with image-analysis software every day -- this is an interactive hands-on school, not a passive lecture series. Short introductions are followed by guided work-flows that we step through together. You can and should ask questions at any time throughout.


    There will be several invited lectures, alternating between scientists using image-analysis as an integral part of their biomedical research and researchers developing new image-analysis algorithms and software.


    You will have the chance to work on your own data, that you brought from home, throughout the week, with support from the trainers.


    Printed material as well as online material will be made available for all the modules.


    1. We will accept 25 participants --- this number is kept low to facilitate effective tutoring.
    2. The school takes place in Zurich
    3. You will need to bring your own laptop (let us know if this is not possible for you)
    4. Participation is only possible for the entirety of the school
    5. A small number or travel grants and fee-waivers are available, mainly for non-Swiss people
    6. PhD students can earn two ECTS points from the school
    7. Lunch will be provided, other meals are up to you
    8. Travel and accommodation arrangements are your own responsibility
  • Previous Editions

    Links to webpages from former schools, with program, trainers, speakers, etc

    ZIDAS2017 participants on top of ETH

    10th - 15th July 2022, Lausanne, Switzerland

    ZIDAS 2021, still hiding from COVID

    27th September-1st October 2021, Switzerland (online)

    ZIDAS2020 participants (some of them) in ZOOM

    29th June-3rd July, Lausanne, Switzerland (online)

    ZIDAS2019 participants on top of ETH

    18th - 23rd June 2019, Zurich, Switzerland

    ZIDAS2018 participants on top of ETH

    19th - 24th June 2018, Zurich, Switzerland

    ZIDAS2017 participants on top of ETH

    16th - 20th October 2017, Zurich, Switzerland

  • Program

    This may change slightly as we go - adapting to participants

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    Click on image to go to linked Google sheet

    Current as of 2017-10-20

  • Intranet

    If you are participant, here you can find additional information

    October 9, 2017
  • Anyone can apply - irrespective of affiliation, position, or location!

    Not yet accepting applications (patience)!

  • Speakers

    Invited (alphabetical)

    • Damian Brunner, University of Zurich, Switzerland
    • Karsten Weis, D-BIOL, ETH Zurich, Switzerland

    Confirmed (alphabetical)

    • Anna Kreshuk, University of Heidelberg, Germany 
    • César Nombela-Arrieta, University Hospital, Zurich, Switzerland
  • Speakers

    Scientists using or developing image analysis methods in their research

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    In our studies we combine modern real-time fluorescence imaging of the living organism with classical genetics, molecular biology, biochemistry and biophysics approaches. Quantitative data analysis is a central tool that we use to describe the dynamics and behaviour of our cellular systems in the wild type and in mutants. Where possible we use these quantitative data to develop mathematical models, which we can use for further “in silico” exploration of the mechanisms driving the system. Consequently, many of our projects are highly collaborative and interdisciplinary.

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    I am interested in automating large-scale analysis of 3D Electron Microscopy volumes of neural tissue. Methodologically, this amounts to machine learning-based image processing. In particular, I am currently working on the introduction of higher-level priors into neuron segmentation algorithms. Previously, I did some research on synapse detection and segmentation and this topic is still of great interest to me.

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    Our lab is interested in studying how the heterogeneous constituents of mammalian bone marrow (BM) tissues are structurally and functionally interconnected to work as a single finely-tuned, sophisticated and versatile functional unit.

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    My research focuses on developing computational methods for analysing medical images. The goals are to

    • extract semantic information, 
    • perform quantitative measurements and 
    • perform population comparisons 

    to aide diagnosis, treatment and clinical research.

  • Speakers

    Invited (alphabetical)

    • Sebastian Kozerke, D-ITET, ETH Zurich, Switzerland
    • Ender Konukoglu, D-ITET, Zurich, Switzerland
    • Gregory Paul, D-ITET, Zurich, Switzerland

    Confirmed (alphabetical)

  • Advisory Board

    Zurich Image and Data Analysis School

  • Calendar

    Here you can check what is on the menu!

  • Social Feed

    Check out our latest updates!

  • Current and past Support and Endorsements

    This school was created by ScopeM-IDA staff, co-organized with BIOP, University of Basel, and FMI for Biomedical Research staff, powered by NEUBIAS trainers, and supported by EXCITE.

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  • Connect With Us

    Something unclear? Let us know!