Image Analysis Lab Course

This repo hosts materials and a website for the Image Analysis module of the lab course held by the Single Cell Unit at the D-BSSE of ETH Zurich.

It hosts a github pages website at https://bsse-scf.github.io/lab_course_image_analysis/book/landing.html.

Course environment setup on RRP for the exercises

Build the course RRP project

1. Log in to the RRP server https://rrp.ia-res.ethz.ch with your user credentials (handed out).
2. Click the [+] button next to Your projects and click Create new project from Git repository.
3. Specify the repository URL https://github.com/bsse-scf/lab_course_image_analysis.git, then click on Clone.
4. Type latest to the field below Branch / Tag / commit, the project name you can leave unchanged. Next click on Build. This will build the Project image from the repository. This step will take several minutes to complete.

Start the course RRP Project

1. Once the build of the project is complete, it will be listed under Your projects, with Status: not running.
2. In the Details tab under Resources enter Memory in Gb 4.0 and hit Enter on the keyboard then under Number of CPUs enter 1.0 and hit Enter on the keyboard.
3. Click the ▶ button to start the project. Status will switch to processing and then to running.
4. Click the [↗] button to start the JupyterLab UI in a new browser tab/window.

Note: For help see the RRP documentaion: https://rrp.ethz.ch/docs/create-project/ or https://rrp.ethz.ch/docs/start-project/

After starting your RRP course project scu_lab_course_ia, you will find four folders in the JupyterLab file browser:

build   # Should not be modified.
openbis # In here you find the necessary data for the course.
project # In this folder, you find all the course material.
results # This is the folder where you should save your results.

Navigate to the project folder and start with a new notebook for your assignments (File -> New -> Notebook). To load the required data set plate01.nd2 you can run the following lines in your notebook:

from pathlib import Path
base = Path.home()
Path(base / 'openbis/data/20260819080658667-36/original/plate01.nd2')

For exercises with ImageJ/Fiji or napari in JupyterLab click top right onto File -> New Launcher -> Desktop. This will start a Linux Desktop in a new Tab of your browser. Then for example click onto the Fiji icon on the Desktop to start the application.

Local usage

To download the dataset manually for your local installation type:

pixi run python data/download_challenge_data.py USERNAME

it will the prompt you for the password and download the plate.01.nd dataset to data/challenge_data for you to use.

AI course tutor

JupyterLab ships with a chat (the speech-bubble icon in the left sidebar, or File -> New -> Chat). Its Course Tutor answers questions about the notebooks the way a teaching assistant would: with hints, questions and explanations rather than finished solutions. Every message you send also carries the notebook cell you currently have selected - including its output or error - so click the cell you are stuck on and ask, e.g. "why does this fail?". Quicker still: the 🎓 button - on each cell and in the notebook toolbar - asks the tutor to explain the selected cell. The tutor can read your notebook but cannot change or run it.