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4	4	Classic [[ilastik>>https://www.ilastik.org/]] is a simple, user-friendly desktop tool for **interactive image classification, segmentation and analysis**. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed **lazily**, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode. Using it requires no experience in image processing.
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6	6	[[webilastik>>https://app.ilastik.org/]] is a web version of ilastik's Pixel Classification Workflow, integrated with the ebrains ecosystem. It can access the data-proxy buckets for reading and writing (though reading is still suffering from latency issues). It uses Neuroglancer as a 3D viewer as well as compute sessions allocated from the CSCS infrastructure.
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8		-== How to use webilastik ==
	9	+== How to use IlastikWeb ==
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10	10	=== Opening a sample Dataset ===
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12	12	Go to [[app.ilastik.org/>>app.ilastik.org/]] and load a [[Neuroglancer Precomputed Chunks dataset>>https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed]]. You can e.g. use a sample data set that is already in the server by pasting the following URL into Neuroglancer's prompt:
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14	14	precomputed:~/~/https:~/~/app.ilastik.org/public/images/c_cells_2.precomputed
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16	16	[[image:image-20220125164204-2.png]]
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18	18	If your dataset is 2D like in the example, you can click the "switch to xy layout" button at the top-right corner of the top-left quadrant of the viewport to use  asingle, 2D viewport:
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20	20	[[image:image-20220125164416-3.png]]
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	27	+== ==
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22	22	which will change the view to something like this:
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24	24	[[image:image-20220125164557-4.png]]
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	34	+== ==
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26	26	== Training the Pixel Classifier ==
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28	28	=== Selecting Image Features ===
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30	30	Pixel Classification uses different characteristics ("features") of your image to determine which class each pixel should belong to. These take into account, for example, color and texture of each pixel as well as that of the neighboring pixels. Each one of this characteristics requires some computational power, which is why you can select only the ones that are sensible for your particular dataset.
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34	34	You can read more about image features in [[ilastik's documentation.>>https://www.ilastik.org/documentation/pixelclassification/pixelclassification\]]
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36	36	The following is an arbitrary selection of image features. Notice that the checkboxes marked in orange haven't been commited yet; Click Ok to send your feature selections (or deselections) to the server.
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38	38	[[image:image-20220125171850-7.png]]
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40	40	=== Labeling the image ===
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42	42	In order to classify the pixels of an image into different classes (e.g.: 'foreground' and 'background') ilastik needs you to provide it with samples of each class.
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44	44	To do so, first select a particular resolution of your dataset (your viewer might interpolate between multiple scales of the dataset, but ilastik operates on a single resolution):
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50	50	[[image:image-20220125165832-2.png]]
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52	52	You must have the "training" tab as the frontmost visible tab in order to start adding brush strokes (in neuroglancer you can click the name of the raw data tab to hide it, for example):
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54	54	[[image:image-20220125170609-3.png]]
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56	56	The status display in this applet will show "training on [datasource url]" when you're in training mode.
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58	58	Now you can start adding brush strokes. Select a color from the color picker, check the "Enable Brushing" checkbox to enable brushing (and disable navigation), and click and drag over the image to add brush strokes. Ilastik will map each used color to a "class", and will try to figure out a class for every pixel in the image based on the examples provided by the brush strokes. By painting, you provide ilastik with samples of what a pixel in that particular class should look like. The following image shows an example with 2 classes: teal, representing the "foreground" or the "cell class", and magenta, representing the "background" class.
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64	64	[[image:image-20220125171715-5.png]]
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66	66	You can adjust the display settings of the overlay predictions layer as you would in vanilla neuroglancer:
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68	68	1. right-click the predictions Neuroglancer tab to reveal the "rendering" options