A workflow is a set of components assembled in some specific order to

  1. Measure and estimate some numerical parameters of the biological system or
  2. Visualization

for addressing a biological question. Workflows can be a combination of components from the same or different software packages using several scripts and manual steps.


QuantiFish is a quantification program intended for measuring fluorescence in images of zebrafish, although use with images of other specimens is possible. This package is geared towards analysis of fluorescent infection models. The software is designed to automate processing of images of single fish, and outputs results as a .csv file. Alongside measures of total fluorescence above a threshold, this package also introduces several measures for dissemination and distribution of fluorescence throughout the specimen.

QuantiFish User Interface

Set of KNIME workflows for the training of a deep learning model for image-classification with custom images and classes.

The workflows take ground-truth category annotations as a table generated by the qualitative annotations plugins in Fiji.

Workflows for the training of a model AND for the prediction of image-category for new images are provided.

There are different workflows if you do:

- binary image-classification (images get classified in 1 category out of 2 possible categories) 

- classification from possibly more than 2 categories (images are classified in 1 category out of N possible categories).

The training workflows take care of image pre-processing and allows the visualization of the training and validation losses in real time along the training.  

For the training, transfer learning from a pre-trained VGG16 base is performed, with freshly initialized fully connected layers.

Only the fully connected layers are trained, the VGG16 base is frozen is this workflow, but once the fully connected layers trained the base could also be finetuned. In practice, it often works well with the frozen base.

has function

Analyze the clustering behavior of nuclei in 3D images. The centers of the nuclei are detected. The nuclei are filtered by the presence of a signal in a different channel. The clustering is done with the density based algorithm DBSCAN. The nearest neighbor distances between all nuclei and those outside and inside of the clusters are calculated.

has function

Deep learning based image restoration methods have recently been made available to restore images from under-exposed imaging conditions, increase spatio-temporal resolution (CARE) or self-supervised image denoising (Noise2Void). These powerful methods outperform conventional state-of-the-art methods and leverage down-stream analyses significantly such as segmentation and quantification.

To bring these new tools to a broader platform in the image analysis community, we developed a simple Jupyter based graphical user interface for CARE and Noise2Void, which lowers the burden for non-programmers and biologists to access these powerful methods in their daily routine.  CARE-less supports temporal, multi-channel image and volumetric data and many file formats by using the bioformats library. The user is guided through the different computation steps via inline documentation. For standard use cases, the graphical user interface exposes the most relevant parameters such as patch size and number of training iterations, while expert users still have access to advanced parameters such as U-net depth and kernel sizes. In addition, CARE-less provides visual outputs for training convergence and restoration quality. Any project settings can be stored and reused from command line for processing on compute clusters. The generated output files preserve important meta-data such as pixel sizes, axial spacing and time intervals.

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