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1 -4. How to use Webilastik
1 +4. How to use webilastik
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1 -== What is Webilastik? ==
1 +== What is webilastik? ==
2 2  
3 3  
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.
5 5  
6 +
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.
7 7  
8 -== How to use Webilastik ==
9 +== How to use IlastikWeb ==
9 9  
10 -(% class="wikigeneratedid" %)
11 -Webilastik is a web application that can be accessed on [[https:~~/~~/app.ilastik.org>>https://app.ilastik.org]]. We suggest using it via the Chrome (or Chromium) web browser for now, since most of the testing has been done in this browser and subtle differences between browsers might cause unexpected behavior in the application.
11 +=== Opening a sample Dataset ===
12 12  
13 -(% class="wikigeneratedid" %)
14 -You can find the webilastik application at [[https:~~/~~/app.ilastik.org/>>url:https://app.ilastik.org/]]. You can also go directly to the [[application page>>https://app.ilastik.org/public/nehuba/index.html#!%7B%22layout%22:%22xy%22%7D]].
13 +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:
15 15  
16 -Webilastik is an overlay on top of other data viewers. In particular, this implementation uses [[Neuroglancer>>https://github.com/google/neuroglancer]] as an underlying data viewer, so if you're familiar with its controls you can still use them when using webilastik.
17 17  
18 -=== Moving the controls window ===
19 -
20 -You can move the webilastik controls around the screen by clicking and dragging on the header:
21 -
22 -[[image:webilastik_click_and_drag.png]]
23 -
24 -
25 -=== Opening a Dataset ===
26 -
27 -Like in vanilla Neuroglancer, you add datasets to the viewer by clicking the "+" button at the top of the viewer:
28 -
29 -[[image:webilastik_click_plus_sign_in_neuroglancer.png||height="200"]]
30 -
31 -
32 -You should be presented with a popup prompt where you can type in the URL of a dataset you want to view, in the format typically used by Neuroglancer. There are a few sample datasets hosted in webilastik:
33 -
34 -precomputed:~/~/https:~/~/app.ilastik.org/public/images/mouse1.precomputed
35 -
36 -precomputed:~/~/https:~/~/app.ilastik.org/public/images/mouse2.precomputed
37 -
38 -precomputed:~/~/https:~/~/app.ilastik.org/public/images/mouse3.precomputed
39 -
40 40  precomputed:~/~/https:~/~/app.ilastik.org/public/images/c_cells_2.precomputed
41 41  
42 -precomputed:~/~/https:~/~/app.ilastik.org/public/images/c_cells_3.precomputed
43 43  
44 -
45 -After you type or paste the URL into the "Source" field, neuroglancer should recognize the shape and number of channels in the image. You can the click "Add Layer" to open the dataset in the viewer.
46 -
47 47  [[image:image-20220125164204-2.png]]
48 48  
49 49  
50 -=== Opening a Dataset from the data-proxy ===
22 +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:
51 51  
52 -You can also load Neuroglancer Precomputed Chunks data from the data-proxy; The URLs for this kind of data follow the following scheme:
53 -\\##precomputed:~/~/https:~/~/data-proxy.ebrains.eu/api/buckets/(% style="background-color:#3498db; color:#ffffff" %)my-bucket-name(% style="background-color:#9b59b6; color:#ffffff" %)/path/inside/your/bucket(%%)##
54 54  
55 -So, for example, to load the sample data inside the (% style="background-color:#3498db; color:#ffffff" %)quint-demo(%%) bucket, under the path (% style="background-color:#9b59b6; color:#ffffff" %)tg-ArcSwe_mice_precomputed/hbp-00138_122_381_423_s001.precomputed(% style="color:#000000" %) (%%) like in the example below:
56 -
57 -
58 -[[image:image-20220128142757-1.png]]
59 -
60 -=== ===
61 -
62 -you would type a URL like this:
63 -
64 -
65 -##precomputed:~/~/https:~/~/data-proxy.ebrains.eu/api/buckets/(% style="background-color:#3498db; color:#ffffff" %)quint-demo(%%)/(% style="background-color:#9b59b6; color:#ffffff" %)tg-ArcSwe_mice_precomputed/hbp-00138_122_381_423_s001.precomputed(%%)##
66 -
67 -this scheme is the same whether you're loading data into the Neuroglancer viewer or specifying an input URL in the export applet.
68 -
69 -=== Viewing 2D Data ===
70 -
71 -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 a single, 2D viewport:
72 -
73 73  [[image:image-20220125164416-3.png]]
74 74  
27 +== ==
28 +
75 75  which will change the view to something like this:
76 76  
31 +
77 77  [[image:image-20220125164557-4.png]]
78 78  
79 -You can also click the
34 +== ==
80 80  
81 -==== A Note on Neuroglancer and 2D data ====
82 -
83 -
84 -Neuroglancer interprets all data as 3D, and visualizing a 2D image is interpreted as a single flat slice of data in 3D space. Scrolling in Neuroglancer can make the viewer go past this single slice of data, effectively hiding it from view. You can see the current viewer position in the top-left corner of the viewport, and you can edit those coordinates to reset the viewer to a position where your data is present and therefore visible (usually z=0 for 2D data):
85 -
86 -
87 -[[image:image-20220222161022-1.png]]
88 -
89 -(% class="wikigeneratedid" %)
90 -Alternatively, once you have a compute session running you can also click the "Reset" button in the lower-right corner of the viewer to move the viewer back to the center of your datasets:
91 -
92 -(% class="wikigeneratedid" %)
93 -[[image:webilastik_click_recenter_button.png]]
94 -
95 -(% class="wikigeneratedid" %)
96 -== Allocating a Compute Session ==
97 -
98 -Normal ilastik operation can be computationally intensive, requiring dedicated compute resources to be allocated to every user working with it.
99 -
100 -
101 -The "Session Management" widget allows you to request a compute session where webilastik will run; Select a session duration and click 'Create' to create a new compute session. Eventually the compute session will be allocated, opening up the other workflow widgets.
102 -
103 -
104 -
105 105  == Training the Pixel Classifier ==
106 106  
38 +
107 107  === Selecting Image Features ===
108 108  
109 109  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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112 112  
113 113  You can read more about image features in [[ilastik's documentation.>>https://www.ilastik.org/documentation/pixelclassification/pixelclassification\]]
114 114  
47 +
115 115  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.
116 116  
117 117  [[image:image-20220125171850-7.png]]
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118 118  
119 119  === Labeling the image ===
120 120  
54 +
121 121  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.
122 122  
123 123  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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128 128  
129 129  [[image:image-20220125165832-2.png]]
130 130  
65 +
131 131  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):
132 132  
133 -[[image:image-20220222151117-1.png]]
68 +[[image:image-20220125170609-3.png]]
134 134  
135 135  
136 136  The status display in this applet will show "training on [datasource url]" when you're in training mode.
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137 137  
138 138  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.
139 139  
140 -[[image:image-20220222153157-4.png]]
75 +[[image:image-20220125171324-4.png]]
141 141  
142 -Once you have some image features selected and some brush annotation of at least 2 colors, you can check "Live Update" and ilastik will automatically use your examples to predict what classes the rest of your dataset should be, displaying the results in a "predictions" tab.
77 +Once you have some image features selected and at least one brush annotation, ilastik will automatically use your examples to predict what classes the rest of your dataset should be, displaying the results in a "predictions" tab.
143 143  
144 -[[image:image-20220222153610-5.png]]
79 +[[image:image-20220125171715-5.png]]
145 145  
81 +
146 146  
147 -You can keep adding or removing brush strokes to improve your predictions.
148 -
149 149  You can adjust the display settings of the overlay predictions layer as you would in vanilla neuroglancer:
150 150  
151 151  1. right-click the predictions Neuroglancer tab to reveal the "rendering" options
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157 157  [[image:image-20220125172238-8.png]]
158 158  
159 159  You can keep adding or removing features to your model, as well as adding and removing annotations, which will automatically update the predictions tab.
160 -
161 -=== Exporting Results and Running Jobs ===
162 -
163 -Once you trained your pixel classifier with the previous applets, you can apply it to other datasets or even the same dataset that was used to do the training on.
164 -
165 -To do so, select a data source by typing in the URL of the data source in the Data Source Url field and select a scale from the data source as they appear beneath the URL field.
166 -
167 -Then, configure a Data Sink, i.e., a destination that will receive the results of the pixel classification. For now, webilastik will only export to ebrains' data-proxy buckets; Fill in the name of the bucket and then the prefix (i.e.: path within the bucket) where the results in Neuroglancer's precomputed chunks format should be written to.
168 -
169 -[[image:image-20220125190311-2.png]]
170 -
171 -Finally, click export button and eventually a new job shall be created if all the parameters were filled in correctly.
172 -
173 -You'll be able to find your results in the data-proxy GUI, in a url that looks something like this:
174 -
175 -https:~/~/data-proxy.ebrains.eu/your-bucket-name?prefix=your/selected/prefix
176 -
177 -[[image:image-20220125191847-3.png]]
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