Changes for page 4. Image segmentation with ilastik
Last modified by annedevismes on 2021/06/08 11:56
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edited by annedevismes
on 2021/06/08 10:57
on 2021/06/08 10:57
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... ... @@ -1,35 +1,22 @@ 1 -== [[image:ilastik_logo.PNG||style="float:right"]]==1 +== Analysis approach for series of rodent brain section image == 2 2 3 - == [[image:Pixel_classificationworkflow.png||style="float:left"]](%style="color:#c0392b"%)Analysisapproach forseries of rodent-brain section image(%%) ==3 +There are two main approaches for the analysis of rodent brain section images. 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 +1. Pixel classification only (with two or more classes) 6 +1. Pixel classification with two classes (//immunoreactivity// and //background//), followed by object classification with two classes (//objects-of-interest// and //artefact//). 6 6 7 -**There are two main approaches for the analysis of rodent-brain section image~:** 8 - 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 - 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(labelling)versus the background and other structures. If there is non-specific labelling in the image that is very similar in appearance to the labellingon thebasisoftheobject-level features such as shape and size. The best approach is determined by trial and error.10 +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. 15 15 16 -=== (% style="color:#c0392b" %)Pixel classification workflow(%%)===12 +=== Pixel classification workflow === 17 17 18 -For a quick introduction, [[watchthisideo>>https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]].14 +For a quick introduction, watch: [[https:~~/~~/www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be>>url:https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]] 19 19 20 - **Basicsteps**16 +=== Object classification workflow === 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. 25 -* Review the results. 26 26 27 -=== (% style="color:#c0392b"%)Object-classificationworkflow(%%)===19 +==== ==== 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. 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. 35 -* Review the results. 22 +
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