PrerequisitesBefore starting this lesson, you should be familiar with:
Learning ObjectivesAfter completing this lesson, learners should be able to:
Create a basic image analysis workflow.
Understand that bioimage analysis workflows consist of a sequence of image analysis components.
Segment nuclei in a 2D image and measure their shapes and understand the components (concepts and methods) that are needed to accomplish this task.
Draw a biophysically meaningful conclusion from applying an image analysis workflow to a set of images.
Detecting a set of objects in an image, counting them and measuring certain characteristics about their morphology is probably the most frequently occurring task in bioimage analysis. Depending on the image, even this task could become quite challenging and the workflow could become quite complex. Here we start with a relatively simple image where combining a minimal set of image analysis components into a simple workflow does the job.
- Input images
- The images are two time points of a time lapse experiment where the INCENP gene was subjected to siRNA knock-down. The data are taken from the published mitocheck screen. In this screen the authors carried out a genome-wide phenotypic profiling of each of the ~21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed them to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. The analysis that we apply here is, of course, simpler than what the authors did in the publication, but the essence is already very similar. In addition, to simplify the task we work here on images that were cropped and slightly denoised.
- Apply the workflow outlined above (see concept map) to both images.
- The modules listed in “Prerequisites” contain the information as to how to conduct each step of the workflow.
- The nuclei in both images look quite different. Find shape measurements that quantify this.
- The workflow contains a thresholding step; choosing this threshold is somewhat arbitrary; try different thresholds and explore how this affects the measurements.
Show activity for:
Process › Binary › Options...
[X] Black background, because we work with fluorescence data
- Open one of the above images
Image › Duplicate...
Title = binary
- Draw a line profile to find a good threshold
- Use the straight line tool in the Fiji menu bar
Analyze › Plot Profile
Liveand move the line around, including nuclei and background pixels
Image › Adjust › Manual Threshold...
Min = 25
Max = 255, because this is the maximum of the image data-type
Process › Binary › Convert to Mask
Plugins › MorphoLibJ › Binary Images › Connected Components Labeling
connectivity = 4, for no good reason…
type = 8 bits, because we will have less than 255 objects
Plugins › MorphoLibJ › Analyze › Analyze Regions
- You may subset the measurements if you are not interested in all
skimage and napari
Recommended follow-up modules: