

Identifying objects in images acquired by electron microscopy (EM) can be challenging. Since contrast and intensity distributuion in EM images is generally low, simple segmentation algorithms which are based on intensity thresholds or contrast detection often fail with such datasets. This issue is especially pronounced in tissue samples where cells or organelles with comparable density boarders lie close together. This makes the discrimination of neighboring objects difficult because intensity differences and contrast are low. There are, however ways to cope with these images. One is to manually segment the objects of interest by encircling them by hand, plane by plane, and by fusing these segments at the end. While this method bring success, it is very tedious and time consuming.
Automatic algorithms and machine learning (trainable segmenters) can speed up the process of segmenting EM images enormously. To make these approaches successful, it is necessary to crudely isolate the objects of interest first in a manual step.
Here, we want to demonstrate an approach to segment mitochondria in a 3D transmission EM image by manually pre-segmenting the image using Virtual Reality and subsequently identifying mitochondria in the pre-segmented image. This method brings considerably faster results than manual segmentation procedures and can achieve high accuracy.
Faster results and higher accuracy
In order to segment objects, a seeding object is placed quickly and accurately in Virtual Reality. This is done using the the “Create Segments” tool to place spheres in all mitochondria. To get a better overview, one can adjust the Color Transfer Function in a way to show the image in a semi-transparent way. Also, activate left hand clipping in the Tool menu of the Clipping tool to dynamically clip through the image and keep a good overview of the image.
Using the sculpting tool, you select all individual seeding objects and expand them to encompass the entire mitochondria in 3D. It is not important to draw precisely along the border of the organelle but it is essential to cover the whole structure with the manually drawn segment. Again, use the left hand clipping tool to check the result.
Use the “Switch to Vision4D” functionality to open the dataset in arivis Vision4D which will display the segments created in the InViewR.
Open a new Analysis Pipeline and use the Segments you created in VR as a mask to run a subsequent analysis operator.
In this case, we use a machine learning approach to segment the mitochondria. For this, foreground and background features have to be determined by manually drawing them on a 2D plane and adding them to the list.
In order to train the machine learning algorithm, it needs a set of classifier features to work with. Click on the button and choose a set of classifiers Classifiers are pixel features that will be used afterwards to discriminate between a pixel being part of an object of interest or not. You can choose between Color, Edge (Contrast), Texture and Orientation as such features. The different size values indicate the size of the Gaussian blur that is added before the filter is applied. By doing so, it is possible to identify pixel classes on a larger scale. If you are uncertain what classifier to use, you can always select all. This however will slow down the process and result in an increased processing time.
Train the Operator based on the information it has. To see a preview, click on the eye button within the operator. You can adapt the result by adjusting the threshold and smoothing sliders on the left. If the result is unsatisfying, try to add more foreground and background features as well as an extended set of classifiers.
Run the analysis to see the overall result. A filtering step can also be added to clean up the result.
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