4. Image segmentation with ilastik

== [[image:ilastik_logo.PNG||style="float:right"]] ==

== [[image:Pixel_classification workflow.png||style="float:left"]](% style="color:#c0392b" %)Analysis approach for series of rodent-brain section image(%%) ==

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//Ilastik //is a versatile image analysis tool specifically designed for the classification, segmentation, and analysis of biological images based on supervised machine-learning algorithms.

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**There are two main approaches for the analysis of rodent-brain section image~:**

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1. pixel classification only (with two or more classes) and

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1. pixel classification with two classes (//immunoreactivity// and //background//), followed by Object classification with two classes (//objects of interest// and //artefact//).

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**Which approach is best for my dataset?**

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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 (labelling) 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 on the basis of the object-level features such as shape and size. The best approach is determined by trial and error.

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=== (% style="color:#c0392b" %)Pixel classification workflow(%%) ===

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For a quick introduction, [[watch this video>>https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]].

**Basic steps**

* Train the classifier with two classes (labelling and background).

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* Apply the classifier to the rest of the images (batch processing).

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* Export the probability maps in HDF5 format and simple_segmentation images in PNG format with the default settings.

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* Review the results.

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=== (% style="color:#c0392b" %)Object-classification workflow(%%) ===

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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.

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* 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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* In the "Input Data" applet, upload the original images and their respective probability maps in HDF5 format (output from the Pixel Classification).

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* Train the classifier with two classes (labelling and artefacts).

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* In the "Object Information Export" applet, export “Object Predictions” in PNG format. Do not change the default export location.

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* Review the results.

Wiki source code of 4. Image segmentation with ilastik

QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain