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Last modified by annedevismes on 2021/06/08 11:56
From version 20.1
edited by puchades
on 2020/09/24 12:17
Change comment: There is no comment for this version
To version 20.2
edited by annedevismes
on 2021/06/08 10:57
Change comment: There is no comment for this version

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1 -XWiki.puchades
1 +XWiki.annedevismes
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1 1  == [[image:ilastik_logo.PNG||style="float:right"]] ==
2 2  
3 -== [[image:Pixel_classification workflow.png||style="float:left"]](% style="color:#c0392b" %)Analysis approach for series of rodent brain section image(%%) ==
3 +== [[image:Pixel_classification workflow.png||style="float:left"]](% style="color:#c0392b" %)Analysis approach for series of rodent-brain section image(%%) ==
4 4  
5 -Ilastik is a versatile image analysis tool specifically designed for the classification, segmentation and analysis of biological images based on supervised machine learning algorithms.
5 +//Ilastik //is a versatile image analysis tool specifically designed for the classification, segmentation, and analysis of biological images based on supervised machine-learning algorithms.
6 6  
7 -**There are two main approaches for the analysis of rodent brain section image**
7 +**There are two main approaches for the analysis of rodent-brain section image~:**
8 8  
9 -1. Pixel classification only (with two or more classes)
10 -1. Pixel classification with two classes (//immunoreactivity// and //background//), followed by Object classification with two classes (//objects of interest// and //artefact//).
9 +1. pixel classification only (with two or more classes) and
10 +1. pixel classification with two classes (//immunoreactivity// and //background//), followed by Object classification with two classes (//objects of interest// and //artefact//).
11 11  
12 12  **Which approach is best for my dataset?**
13 13  
14 -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 based on object level features such as shape and size. The best approach is determined by trial and error.
14 +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.
15 15  
16 16  === (% style="color:#c0392b" %)Pixel classification workflow(%%) ===
17 17  
18 18  For a quick introduction, [[watch this video>>https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]].
19 19  
20 -**Basic steps:**
20 +**Basic steps**
21 21  
22 -* Train the classifier with two classes (labelling and background)
23 -* Apply the classifier to the rest of the images (batch processing)
24 -* Export the probability maps in HDF5 format, and simple_segmentation images in PNG format with the default settings.
22 +* Train the classifier with two classes (labelling and background).
23 +* Apply the classifier to the rest of the images (batch processing).
24 +* Export the probability maps in HDF5 format and simple_segmentation images in PNG format with the default settings.
25 25  * Review the results.
26 26  
27 -=== (% style="color:#c0392b" %)Object classification workflow(%%) ===
27 +=== (% style="color:#c0392b" %)Object-classification workflow(%%) ===
28 28  
29 -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//**.**
29 +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.
30 30  
31 -* 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.
32 -* In the **Input Data** applet, upload the original images and their respective probability maps in HDF5 format (output from the Pixel Classification).
33 -* Train the classifier with two classes (labelling and artefacts)
34 -* In the **Object Information Export** applet, export “Object Predictions” in PNG format.  Do not change the default export location.
31 +* 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.
32 +* In the "Input Data" applet, upload the original images and their respective probability maps in HDF5 format (output from the Pixel Classification).
33 +* Train the classifier with two classes (labelling and artefacts).
34 +* In the "Object Information Export" applet, export “Object Predictions” in PNG format.  Do not change the default export location.
35 35  * Review the results.