4. How to use Webilastik

Version 45.1 by tomazvieira on 2022/09/11 15:58

What is Webilastik?

Classic ilastik 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.

webilastik 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.

How to use Webilastik

Webilastik is a web application that can be accessed on 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.

You can find the webilastik application at https://app.ilastik.org/. You can also go directly to the application page.

Webilastik is an overlay on top of other data viewers. In particular, this implementation uses Neuroglancer as an underlying data viewer, so if you're familiar with its controls you can still use them when using webilastik.

Moving the controls window

You can move the webilastik controls around the screen by clicking and dragging on the header:

Opening a Dataset

Like in vanilla Neuroglancer, you add datasets to the viewer by clicking the "+" button at the top of the viewer:

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:

precomputed://https://app.ilastik.org/public/images/mouse1.precomputed

precomputed://https://app.ilastik.org/public/images/mouse2.precomputed

precomputed://https://app.ilastik.org/public/images/mouse3.precomputed

precomputed://https://app.ilastik.org/public/images/c_cells_2.precomputed

precomputed://https://app.ilastik.org/public/images/c_cells_3.precomputed

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.

Opening a Dataset from the data-proxy

You can also load Neuroglancer Precomputed Chunks data from the data-proxy; The URLs for this kind of data follow the following scheme:

precomputed://https://data-proxy.ebrains.eu/api/buckets/my-bucket-name/path/inside/your/bucket

So, for example, to load the sample data inside the ana-workshop bucket, under the path tg-ArcSwe_mice_precomputed/hbp-00138_122_381_423_s001.precomputed like in the example below:

you would type a URL like this:

precomputed://https://data-proxy.ebrains.eu/api/buckets/ana-workshop/tg-ArcSwe_mice_precomputed/hbp-00138_122_381_423_s001.precomputed

this scheme is the same whether you're loading data into the Neuroglancer viewer or specifying an input URL in the export applet.

Viewing 2D Data

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:

which will change the view to something like this:

You can also click the

A Note on Neuroglancer and 2D data

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):

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:

Allocating a Compute Session

Normal ilastik operation can be computationally intensive, requiring dedicated compute resources to be allocated to every user working with it.

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.

Training the Pixel Classifier

Selecting Image Features

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.

Use the checkboxes in the applet "Select Image Features" applet to select some image features and their corresponding sigma (which determines the radius around the pixel that will be considered when computing that feature).

You can read more about image features in ilastik's documentation.

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.

Labeling the image

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.

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):

Once you've selected a resolution to train on, you should see a new "training" tab at the top of the viewer:

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):

The status display in this applet will show "training on [datasource url]" when you're in training mode.

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.

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.

You can keep adding or removing brush strokes to improve your predictions.

You can adjust the display settings of the overlay predictions layer as you would in vanilla neuroglancer:

right-click the predictions Neuroglancer tab to reveal the "rendering" options
Adjust the layer opacity to better view the predictions or underlying raw data;
Advanced users: edit the shader to render the predictions in any arbitrary way;

The image below shows the "predictions" tab with an opacity set to 0.68 using the steps described above:

You can keep adding or removing features to your model, as well as adding and removing annotations, which will automatically update the predictions tab.

Exporting Results and Running Jobs

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.

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.

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.