Changes for page 4. Image segmentation with ilastik
Last modified by annedevismes on 2021/06/08 11:56
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... ... @@ -1,40 +1,30 @@ 1 -== Analysisapproach forseriesofrodentbrain sectionimage==1 +== H2 Headings Will Appear in Table of Content == 2 2 3 -There are two main approaches for the analysis of rodent brain section images. 4 4 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//). 4 +Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. 7 7 8 - **Whichapproachisbestformydataset?**6 +>This is a quote. You can add a quote by selecting some text and clicking the quote button in the editor. 9 9 10 - As a generalrule,pixelclassification issuitableforimagesinwhichtherearecleardifferencesinthe colour,intensityand/ortextureofthefeature-of-interestversus the backgroundandotherstructures. If thereis non-specificlabellingintheimagethat isverysimilar inappearanceothelabelling-of-interest, objectclassificationmayallow thenon-specificlabellingtobefilteredoutbased on objectlevel featuressuchas size andshape.Thebest approachisdetermined bytrial and error.8 +Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. 11 11 12 -=== Pixelclassificationworkflow===10 +=== H3 Headings Will Appear In The Table of Content === 13 13 14 - Fora quick introduction,watch: [[https:~~/~~/www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be>>url:https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]]12 +==== You can also add images ==== 15 15 16 - **Basicsteps:**14 +[[image:Collaboratory.Apps.Article.Code.ArticleSheet@placeholder.jpg]] 17 17 18 - -Train the classifier with twoclasses (labelingandbackground)16 +Photo by David Clode 19 19 20 - -Applythe classifierto the rest of the images (Batch processing)18 +==== Or code ==== 21 21 22 - -Export theprobability mapsin HDH5 format, andsimple_segmentationimagesin.pngformat, withthe defaultsettings.20 +Code blocks can be added by using the code macro: 23 23 24 --Review our results. 22 +{{code language="python"}} 23 +x = 1 24 +if x == 1: 25 + # indented four spaces 26 + print("x is 1.") 27 +{{/code}} 25 25 26 -=== Object classification workflow === 27 - 28 -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 - 30 --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. 31 - 32 --In the **Input Data** applet, upload the original images and their respective probability maps in HDH5 format (output from the pixel classification). 33 - 34 --Train the classifier with two classes (labeling and artefacts) 35 - 36 -(% class="wikigeneratedid" id="H" %) 37 --In the **Object Information Export** applet, export “Object Predictions” in PNG format. Do not change the default export location 38 - 39 - 29 +(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %) 40 40