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NEUBIAS Academy @HOME: Interactive Bioimage Analysis with Python and Jupyter

This course is an introduction to bioimage processing with Python and Jupyter. It is composed of a series of Jupyter notebooks that can be simply read or run interactively using either the binder service, the Google Colab service or via a local installation. The course covers basics, such as handling images as Numpy arrays, filtering, object measurement etc., as well as a few more advanced topics such as tracking, registration etc. While some content presents fundamental concepts in image processing, this is NOT an image processing course. The goal of this course is to give people with image processing background (e.g. in Fiji) the opportunity to discover how image processing is done in the Python world. In particular this course is not exhaustive as it only covers selected topics.

If some material is unclear or you discover errors, please post an issue on this repository. If you have more general questions related to image processing, please post them on the image.sc forum. We will not provide support for such questions here.

Table of contents

Companion material

This material comes as a complement to two webinars from the NEUBIAS Academy @Home series given on May 7th and 13th 2020 and available on Youtube here and here. Those webinars give a general overview of the different parts of the Python/Jupyter ecosystem for bioimage analysis. In particular they describe:

  • Jupyter notebooks (what they are and how to use them)
  • the possibilities to use remote resources (Binder, Colab)
  • the main packages (Numpy, scikit-image etc.)
  • the principle of environments with a focus on conda
  • live coding demos with notebooks

The presentation used during those webinars is also available as a Google slides here.

Static notebooks

GitHub usually renders notebooks when browsing through the repository. If rendering fails you should use the more stable rendering provided through nbviewer.

Running the notebooks interactively

On Binder

This is the preferred solution. This repository has been set-up to be run interactively without any installation required thanks to the binder service. An interactive Jupyter session can be started by clicking on the binder badge in the top left corner of this document. All the necessary packages and data are already pre-installed. If you want to explore this course via this method, you can stop reading here.

On Colab

Alternatively, you can run these notebooks via Google Colab. To do that, click on the Colab badge in the top left corner of this document. This will bring you to the Colab service where you will be able to select a notebook to open. Note that if you directly download the notebooks from GitHub, you should select the colab branch instead of master.

The dataset cannot be "pre-installed" for you on Colab, so you will have to download it to Google Drive either directly using this link or by visiting the Zendo repository of the course. Unzip the Data.zip file and place the Data folder at the very top of your Google Drive folder tree. The latter is important to ensure that image import commands in the notebooks work as expected.

In order to access Google Drive from Colab, you will need to connect to it from each notebook. For that, execute the following cell at the top of each notebook and follow instructions:

from google.colab import drive
drive.mount('/content/drive')

Finally, as it is not possible to permanently install additional packages in Colab, they are installed in each notebook via a pip command at the top of the notebook. Those cell will look like this (here aicsimageio and trackpy are installed):

%%bash
wget https://raw.githubusercontent.com/guiwitz/neubias_academy_biapy/master/course_functions.py
pip install aicsimageio
pip install trackpy

Local installation

In order to run this course on your own computer you will have to:

  1. Download this GitHub repository by using the "Clone or Download" button at the top right of this page.

  2. Download the dataset for the course either directly using this link or by visiting the Zendo repository of the course.

  3. Adjust your folders so that the Data folder is at the same level as the folder containing the notebooks:

    Mainfolder
     ├── neubias_academy_biapy
     │   ├── 01-Python_bare_minimum.ipynb
     │   ├── 02-Numpy_images.ipynb
     │   └── ...
     ├── Data
     │   ├── neuron.tif
     │   ├── Klee.jpg
     │   └── ...
     └── ...
    
  4. Install Jupyter and all necessary packages. We strongly recommend to install Anaconda or Miniconda (a light-weight Anaconda) to facilitate package management. If you install Anaconda, you can install all necessary packages by using the environment_minimal.yml file available in BIAPy/installation and following these instructions. If you use miniconda or prefer to install packages via command line, open a terminal and:

    1. Move to the neubias_academy_biapy/installation folder e.g.
    cd Mainfolder/neubias_academy_biapy/installation
    1. Use the environment_minimal.yml file to install all packages in one go:
    conda env create -f environment_minimal.yml
    1. Now you have a specific environment called biapy that has all packages (including Jupyter) installed. Every time you want to use it, open a terminal and start Jupyter:
    conda activate biapy
    1. To start a Jupyter session, activate your environment biapy (see 3.) and then start Jupyter:
    jupyter notebook

    Note that the Jupyter browser will open from your current location in the terminal. So don't start jupyter from within e.g. the installation folder as you won't be able to move higher.

Note that this procedure installs all packages except for 13-ML_based_segmentation.ipynb. That notebook requires Cellpose and StarDist. As installation of these packages might depend on your local configuration, we do not include them in the default installation. Consult their documentation on how to install them if you want to explore that notebook.

Additional technical information

Turning a Q&A into an image.sc post

The questions and answers of the live NEUBIAS webinar were saved into a spreadsheet (see e.g here) and exported as a CSV file. To generate the post that can be found here, the CSV file was transformed into a Markdown document through the Jupyter notebook questions_to_markdown.ipynb. You can also run that notebook from Binder and upload your own CSV file to turn it into Markdown. Note that the image.sc forum markdown requires links to be html tags, simple Markdown links don't work.

Automated conversion for Colab

There are a three main differences in the requirements to run notebooks on Binder or Colab: the path to data is different (as data are stored on Google Drive for Colab), the small course module course_function.py needs to be copied to Colab, and some packages need to be installed on Colab. The notebook automate_colab_editing.ipynb automates the conversion from Binder to Colab: 1. It checks each notebook for package imports and adds a cell to install missing packages, 2. it checks for data imports, and changes the paths to Google Drive format, and 3. it adds a cell to download the course module. Each notebook is then reexported into another folder. Ideally that folder corresponds to a specific branch of the repository.

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