Bioimage informatics

Bioimage analysis


LaRoME = Label + Region Of Interest + Measure

Label image (aka Count Masks): An image in which pixels of an object have all the same value. Each object has a unique value.

Measurement image: An image in which pixels of an object have all the same value, corresponding to a measurement (Area, Angle, Mean...)

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Data Science in Cell Imaging (DSCI) course material

Submitted by assafzar on Thu, 12/31/2020 - 10:08

Graduate course at the department of Software and Information Systems Engineering at Ben-Gurion University of the Negev.

The recent explosion in high-content, dynamic and multidimensional imaging data is transforming cell imaging into a “Data Science” field. This course will review the state-of-the-art in visualizing, processing, integrating and mining massive cell image data sets, deciphering complex patterns and turning them into new biological insight. It will include a mix of approaches in computer science, machine learning and computer vision applied to bio-imaging data.


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.

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Set of Fiji plugins facilitating the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets.

The plugins can be installed by activating the Qualitative annotations plugins in Fiji.


This macro toolset offers additional click tools for the rapid annotations of ROI in ImageJ/Fiji.

The ROI 1-click tools can be setup with a predefined shape, and custom actions to perform upon click (Add to ROI Manager, Run Measure, Go to next slice, run a macro command...)

To install in Fiji, just activate the ROI 1-click tools