How to use the plugin

Microscopic images usually come with a large stack: many high-resolution slices!

There are two ways to utilize this plugin over a large stack:

• One Model for All: Training an RF model on a small sub-stack, then predicting over the entire stack.
• Divide And Conquer: Dividing the large stack into several sub-stacks, then train an RF model for each.

One Model For All

As for the first step, we recommend making a small sub-stack to train a Random Forest (RF) model using our plugin. This sub-stack can have about 20 slices selected across the whole stack (not just the beginning or last few slices). This way, when you extract and save the sub-stack's features, the storage file won't occupy too much space on the hard drive.

Tip

If the image resolution is high, it's better to down-scale images into a resolution of below 1200 pixels for the largest dimension.

After the training, you can save the RF model, and later apply it on the entire stack.

Divide And Conquer

Extracted features saved as an HDF5 file can take a very large space on disk. In this method, to prevent the disk space overflow, you can divide your large stack into several sub-stacks. Then use the plugin for each, separately.
Although, you can try one trained model over another sub-stack, Random Forest model can not be fine-tuned. By using this method, you can achieve better annotations with the expense of spending more time on training several models.