Workflow

This tool allows to analyze morphological characteristics of complex roots. While for young roots the root system architecture can be analyzed automatically, this is often not possible for more developed roots. The tool is inspired by the Sholl analysis used in neuronal studies. The tool creates a binary mask and the Euclidean Distance Transform from the input image. It then allows to draw concentric circles around a base point and to extract measures on or within the circles. Instead of circles, which present the distance from the base point, horizontal lines can be used, which present the distance in the soil from the base-line. The following features are currently implemented:

• The area of the root per distance/depth.
• The number of border pixel per distance/depth, giving an idea of the surface in contact with the soil.
• The maximum radius per distance/depth of a root, measured at the crossing points with the circles or lines.
• The number of crossings of roots with the circles or lines.
• The maximum distance to the left and the right from the vertical axis at crossing points with the circles or lines.

Today, 25% of figures in biomedical publications contain images of various types, e.g. photos, light or electron microscopy images, x-rays, or even sketches or drawings. Despite being widely used, published images may be ineffective or illegible since details are not visible, information is missing or they have been inappropriately processed. The vast majority of such imperfect images can be attributed to the lack of experience of the authors as undergraduate and graduate curricula lack courses on image acquisition, ethical processing, and visualization. 
Here we present a step-by-step image processing workflow for effective and ethical image presentation. The workflow is aimed to allow novice users with little or no prior experience in image processing to implement the essential steps towards publishing images. The workflow is based on the open source software Fiji, but its principles can be applied with other software packages. All image processing steps discussed here, and complementary suggestions for image presentation, are shown in an accessible “cheat sheet”-style format, enabling wide distribution, use, and adoption to more specific needs.

TrackMate based tracker to be used when uploading integer labelled segmentation images, coming from a Deep Learning tool such as stardist. To use this tool efficiently we provide a python notebook to collect/localize the position of cells, this step creates a CSV file which can then be loaded into the Fiji tracker to do particle tracking with TrackMate interface.

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.

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.