Grain finding#

At a Glance - Identifying Objects of Interest#

TopoStats automatically tries to find grains (objects of interest) in your AFM images. There are several steps to this.

  • Height thresholding: We find grains based on their height in the image.

  • Remove edge grains: We remove grains that intersect the image border.

  • Size thresholding: We remove grains that are too small or too large.

  • Optional: U-Net mask improvement: We can use a U-Net to improve the mask of each grain.

Height thresholding#

Grain finding is the process of detecting useful objects in your AFM images. This might be DNA, proteins, holes in a surface or ridges on a surface. In the standard operation of TopoStats, the way we find objects is based on a height threshold. This means that we detect where things are based on how high up they are.

For example, with our example minicircles.spm image, we have DNA that is poking up from the sample surface, represented by bright regions in the image, alongside impurities and proteins, also above the surface:

minicircles image

If we want to select the DNA, then we can take only the regions of the image that are above a certain height threshold (standard deviation - std_dev, absolute - absolute, otsu - otsu).

Here are several thresholds to show you what happens as we increase the absolute height threshold:

height thresholds

Notice that the amount of data decreases, until we are only left with the very highest points.

The aim is to choose a threshold that keeps the data you want, while removing the background and other low objects that you don’t want including. So in this example, a threshold of 0.5 would be best, since it keeps the DNA while removing the background.

There are lots of objects in this mask that we don’t want to analyse, but we can remove those using area thresholds in the next steps. These objects have been detectd because while they are small, they are still high up and above the background.

For more information on the types of thresholding, and how to set them, see the thresholding page.

Remove edge grains#

Some grains may intersect the image border. In these cases, the grain will not be able to have accuracte statistics calculated for it, since it is not fully in the image. Because of this, we have the option of removing grains that intersect the image border with the remove_edge_intersecting_grains flag in the config file. This simply removes any grains that intersect the image border.

Here is a before and after example of removing edge grains:

size_thresholding

Size thresholding#

In our thresholded image, you will notice that we have a lot of small grains that we do not want to analyse in our image. We can get rid of those with size thresholding. This is where TopoStats will remove grains based on their area, leaving only the right size of molecules. You will need to play around with the thresholds to get the right results.

You can set the size threshold using the absolute_area_threshold in the config file. This sets the minimum and maximum area of the grains that you want to keep, in nanometers squared. Eg if you want to keep grains that are between 10nm^2 and 100nm^2, you would set absolute_area_threshold to [10, 100].

size_thresholding

Optional: U-Net mask improvement#

As an additional optional step, each grain that reaches this stage can be improved by using a U-Net to mask the grain again. This requires a U-Net model path to be supplied in the config file.

The U-Net model will take the bounding box of each grain, makes it square, and passees it to a trained U-Net model which makes a prediction for a better mask, which then replaces the original mask.

Here is an example comparing absolute height thresholding to U-Net masking for one of our projects. The white boxes indicate regions where the height threhsold performs poorly and is improved by the U-Net mask.

unet_example

Multi-class masking#

TopoStats supports masking with multiple classes. This means that you could use a U-Net to mask DNA and proteins separately.

This requires a U-Net that has been trained on multiple classes.

Here is an example of multi-class masking using a U-Net which was used for one of our projects.

multi_class_unet_example

Technical details#

Details: Multi-class masking#

Multi class masking is implemented by having each image be a tensor of shape N x N x C, where N is the image size, and C is the number of classes. Each class is a binary mask, where 1 is the class, and 0 is not the class. The first channel is background, where 1 is background, and 0 is not background. The rest of the channels are arbitrary, and defined by how the U-Net was trained, however we conventially recommend that the first class be for DNA (if applicable) and the next classes for other objects.