As the name suggests, the object classification workflow aims to classify full objects, based on object-level features and user annotations. In order to do so, the workflow needs segmentation images besides the usual raw image data. Depending on the availability of these segmentation images, the user can choose between three flavors of object classification workflow, which differ by their input data:
In the current version of ilastik, computations on the training images are not performed lazily – the entire image is processed at once.
This means you can’t use enormous images for training the object classifier.
However, once you have created a satisfactory classifier using one or more small images, you can use the “Blockwise Object Classification” feature to run object classification on much larger images (prediction only – no training.)
This is a combined workflow, which lets you start from the raw data, perform pixel classification as described in Pixel Classification workflow and then thresholding the probability maps to obtain a segmentation.
You should choose this workflow if you have pre-computed probability maps. The data input applet of this workflow expects you to load the probability maps in addition to the raw data:
This workflow should be used if you already have a binary segmentation image. The image should be loaded in the data input applet:
If you already have binary segmentation images, skip this section.
There are two ways to transform a probability map into a segmentation in ilastik and both are covered by the thresholding applet. To see the results of changing the parameter settings in this applet, press the “Apply” button.
First, specify which channel of the probability map you want to threshold (we choose channel 1, as it corresponds to object rather than background probability). The “Selected input channel” layer will show you the channel you selected in the corresponding label color:
After selecting the channel, choose a sigma to smooth the probability map with a Gaussian. The Gaussian can be anisotropic, i.e. sigmas for all dimensions can be different. If you do not want to smooth, just select a very small sigma (like 0.6). You can check the results of the smoothing operation by first activating the “Show intermediate results” checkbox and then looking at the “Smoothed input” layer:
Now, two options are available for the actual thresholding, as shown in the little tab widget “One Threshold/Two Thresholds”.
The “One Threshold” tab performs regular thresholding, followed by the size filter. For debugging purposes, we also provide a view on the thresholded objects before size filtering. This layer is activated by checking the “Show intermediate results” checkbox.
The “Two Thresholds” tab performs hysteresis thresholding with two thresholds: high and low. The high threshold is applied first and the resulting objects are filtered by size. For the remaining objects the segmentation is then relaxed to the level of low threshold. The two levels of thresholding allow to separate the criteria for detection and segmentation of objects and select only objects of very high probability while better preserving their shape. As for the single threshold case, we provide a view on the intermediate results after the application of the high threshold, the size filter and the low threshold. The image below shows the results of the high (detection) threshold in multiple colors overlayed with the results of the low (segmentation) threshold in white:
The last parameter of this applet is the size filter, for which you can specify the minimum and maximum value. For both thresholding methods the end result is shown in the “Final output” layer.
Now that we have obtained a segmentation, we are ready to proceed to the “Object Feature Selection” applet.
This applet finds the connected components (objects) in the provided binary segmentation image and computes user-defined features for each object. If you want to inspect the connected components, activate the “Objects (connected components) layer. If you select any object features, connected component analysis will be performed automatically.
The following dialog will appear if you press the “Select features” button:
The “Standard Object Features” refer to the built-in ilastik features, computed by the vigra library. Unless otherwise specified by the “Coord” prefix, the features are computed on the grayscale values of the pixels that belong to the object. You will also notice features, which can be computed “in the neighborhood”. In that case, the neighborhood of the object (specified by the user at the bottom of the dialog) is found by distance transform and the feature is computed for the object itself and for the neighborhood including and excluding the object. Need more features? Object features are plugin-based and very easy to extend if you know a little Python. A detailed example of a user-defined plugin can be found in the $ILASTIK/examples directory, while this page contains a higher-level description of the few functions you’d have to implement.
Once you have selected the features you like, the applet will proceed to compute them. For large 3D datasets this step can take quite a while. However, keep in mind that most of the time selecting more features at this step is not more expensive computationally. We therefore recommend that you select all features you think you might try for classification and then choose a subset of these features in the next applet.
This applet allows you to label the objects and classify them based on the features, computed in the previous applet. If you want to choose a subset of features, press the “Subset features” button. Adding labels and changing their color is done the same way as in the Pixel Classification workflow. For a particular example, let us examine the data more closely by activating only the “Raw data” layer:
Clearly, two classes of cells are present in the image: one more bright but variable, the other darker and more homogeneous. Hopefully, the two classes can be separated by the grayscale mean and variance in the objects. Let us select these two features and add two labels.
Note, that the list of features now only contains the few features that were selected in the previous applet. To label objects, either simply left-click on them or right-click and select the corresponding option. Right-clicking also allows you to print the object properties in the terminal. To trigger classification, check the “Live Update” checkbox.
If the “Live Update” checkbox is activated, the prediction is interactive and you can receive immediate feedback on your labels. Let us examine the prediction results:
In the low right corner we see a cell (shown by the red ellipse), which was classified as “green”, while it is most probably “red”. Let’s label it “red” and check the results again:
All cells seem to be classified correctly, except one segmentation error, where two cells were erroneously merged (shown by the red ellipse). How could we correct that? We’d have to go back to the thresholding applet, where we performed the segmentation. In the best case, you would have caught this error by examining the thresholding output at the first step. The problem with correcting the segmentation now is that with different thresholds the objects will most probably change shape and thus their features. Besides, some objects might disappear completely, while others appear from the background. ilastik will try to transfer your object labels from the old to the new segmentation, but it will fail in case of disappearance or object division, which is why it’s recommended to not change the segmentation after labels are added. Nevertheless, let us try it for demonstration purposes:
After a slight change in the segmentation (lower) threshold the objects indeed become separated. And the two independent objects are predicted correctly:
Segmentation and connected components analysis in the applets above is performed on the whole dataset simultaneously. While these operations and especially the hysteresis thresholding require a lot of memory, “whole image” processing is sufficient for most 2D images. However, for large 3D image volumes we have to resort to blockwise processing. This applet allows you to experiment with different block and halo sizes on the data you used in the interactive object prediction and, by comparing the “whole image” interactive prediction and blockwise prediction, find the optimal parameters for your data. Let us try to predict our image blockwise:
In the upper right corner, an object is shown for which the blockwise object classification clearly failed. This object, however, will be predicted correctly if we choose a more reasonable block and halo size. Supposing we found such sizes, let us proceed to batch prediction itself
These two applets have the same interface and parameters as batch prediction in Pixel Classification workflow. The only difference is that you started the object classification workflow from binary images or prediction images, you’ll have to provide them here as well: