Most of us study and work at the Bio-image Analysis Technology Development group at the DFG Cluster of Excellence “Physics of Life” at the TU Dresden. We blog about image data science, knowledge exchange and research data management in the life sciences. The contents of this blog are licensed by the respective authors under CC-BY 4.0 license unless a different license is specified.
This blog post explains how to run omero scripts in the BiA-PoL omero server. In this example, we execute a script on the server that runs a 2D Stardist model on a sample image.
This is a blog post about the personal experience of Rike, a post-doc who had just finished her PhD at the University Clinic in Dresden. She spent a two weeks training period at BiA-PoL working with image analysis.
This post will help you to get started with using Python. More specifically, it will help you set up Anaconda environments which can be used to control installed packages effectively.
Last part of this series, this post will teach you how to turn your napari GUI/widget into a napari plugin and publish it on Pypi, so everyone can access it, install it and benefit from your contribution.
After learning how to create GUIs, one may want to integrate them into a pre-existing software. This third part will teach you a few ways design GUIs and embbed them into the popular python image viewer napari, either with the help of the Designer or straight from python functions using magicgui.
Good documentation can be a major obstacle towards getting a tool the visibility and usability it needs to make an impact. This blog entry shows how to autocreate documentation pages from function docstrings using Sphinx and how to have the result hosted on Github pages.
Sometimes, graphical user interfaces (GUIs) become too complex to be handled in pure code. This blog will show ways to create more complex graphical user interfaces using the Qt Designer, which allows to create and configure advanced GUIs in a visual interface and create Python-readable configuration files.
Graphical user interfaces - GUIs - can make using scripts and code much easier, as they allow to access functions in ways that are more intuitive than writing pure code. In this blog entry, you will be introduced on how to create basic GUIs for simple jobs in Python and how to connect elements of your GUI with Pyhton functions.
Jamovi is a fairly new, free and open statistical software. It has a very friendly GUI that serves as a welcome door to anyone who wants to perform statistical tests on their data. It was developed with R, one of the best open programming languages for statistical computing and graphics. This blog post introduces you to this program by performing some statistical tests onto data extracted from a sample image.
Installing python libraries on Windows can be tricky. When using
pip install on a Windows computer that is not regularily visited by a hardcore programmer, a typical error message is “error: Microsoft Visual C++ 14.0 or greater is required.”.
This blog post shows how to deal with it and also hints how to avoid installing software that is not necessary.
Principal feature analysis (PFA) is a method for selecting a subset of features that describe most of the variability in the dataset based on Y. Lu et al. published in ACM, 2007. This sounds oddly similar to principal component analysis (PCA), which is no coincidence as the methodologies are intertwined. PCA also does a similar thing but instead of choosing features that describe the variability to a certain threshold we choose a subset of principal components. …
We will now take a more in depth look at how background subtraction works by showing the top-hat filter and Difference of Gaussian (DoG) filter, which both can achieve background subtraction. In general, we want to use background subtraction if there is a sharp signal we want to isolate from moderate signal that is evenly distributed in the background. Some simple functions allow us to find the background image and subtract it from our original image, only leaving the signals we want to isolate.
Now that we have a grasp of the basics of python it’s time to get started with some proper image analysis! For the purpose of trying out image analysis I have a picture of a tribolium embryo with stained nuclei. It previously was a 3D image but we are working with a maximum projection to keep it simple.
3D segmentation using deep learning is computationally costly, it might be necessary from a practical perspective to do it on computers with powerful graphics processing units (GPUs). One option is to do this in the cloud via Google Colab and therefore it is recommended to take a look at ZeroCostDeepLearning4Microscopy. If you are greedy, as I am, and want to run everything on your own Windows computer, you can follow the instructions provided here.
To get started using python the first step is the installation and there are several ways you can do it. To make it easier for your future self it’s a good idea to set up a virtual environment.
Processing large images with python can take time. In order to accelerate processing, graphics processing units (GPUs) can be exploited, for example using NVidia CUDA. For processing images with CUDA, there are a couple of libraries available. We will take a closer look at cupy, which brings more general computing capabilities for CUDA compatible GPUs, and cucim, a library of image processing specific operations using CUDA. Both together can serve as GPU-surrogate for scikit-image.
For downloading images from the image data resource (IDR), you only need a link, e.g. for requesting the data in tif format. You can then use scikit-image to open the image. In this blog post we show how to browse the IDR programmatically in Python.
Not every computer has a powerful graphics processing unit (GPU) and thus, it might make sense to use cloud computing, e.g. provided by Google. In this blog post I give a short intro into Google Colab and how to enable GPU-accelerated image processing in the cloud using clEsperanto
Bio-image analysis is an emerging field. New technological developments in the middle ground between microscopy and data science emerge rapidly and new ways for processing image data change the way how we do research in biology and biophysics. As it is challenging to keep track of all the new developments…
We acknowledge the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence Physics of Life of TU Dresden.