Analysis approach for series of rodent brain section image
There are two main approaches for the analysis of rodent brain section images.
- Pixel classification only (with two or more classes)
- Pixel classification with two classes (immunoreactivity and background), followed by object classification with two classes (objects-of-interest and artefact).
Which approach is best for my dataset?
As a general rule, pixel classification is suitable for images in which there are clear differences in the colour, intensity and/ or texture of the feature-of-interest versus the background and other structures. If there is non-specific labelling in the image that is very similar in appearance to the labelling-of-interest, object classification may allow the non-specific labelling to be filtered out based on object level features such as size and shape. The best approach is determined by trial and error.
Pixel classification workflow
For a quick introduction, watch: https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be
Basic steps:
-Train the classifier with two classes (labeling and background)
-Apply the classifier to the rest of the images (Batch processing)
-Export the probability maps in HDH5 format, and simple_segmentation images in .png format, with the default settings.
Object classification workflow
There are three options on the ilastik start up page for running Object Classification. Choose the Object Classification with Raw Data and Pixel Prediction Maps as input.
-Save the object classification file in the same folder as the raw images for analysis. If the images are moved after the ilastik file is created, the link between the ilastik file and the images may be lost, resulting in a corrupted file.
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