A workflow for performing image segmentation using Avizo 3d 2022. We train a model and make predictions with deep learning.

1. Start Avizo and select “New Project”

2. On the Project page, select “Open Data…”

3. In the Windows Explorer dialog navigate to your unlabelled microscopy
slides and select all the images (from last to first to ensure correct
ordering of slices) and click “Open”.

4. If you have a large dataset you will be prompted about the “Out-of-Core”
warning. For Deep Learning you must select “Read complete volume into
memory”, then click ”OK”.

5. On the next dialog box, you can adjust the Voxel sizes here if required,
otherwise simply select ”OK”.

6. Now repeat the process for the labelled image masks. These work as tiff
masks, but other formats may also be appropriate. Each unique value in
the mask should be assigned with a class of organelle.
Add a “Color Wash” to visualize the two datasets.

7. Depending on how your data is labelled you may need to apply an
“Arithmetic” filter to the dataset. Here we select all labels greater than 0
using the “Expression” A>0. This removes the background of the tiff file
(which had a pixel value of 0) and groups all other pixel values into the
single class. Other filter expressions might be something like:
A==1+2*(A==2)+3*(A==3) to make 3 classes from 3 pixel values.

8. Now add a “DL Training – Segmentation 2D” module to your training
images “Input (X)”, and training labels “Target (Y)”.

9. Now add a “DL Training – Segmentation 2D” module to your training
images “Input (X)”, and training labels “Target (Y)”.

10. You have the option to adjust any of the ”hyperparameters”. Consult the
documentation or experiment to help find the best combination for your
dataset, but the defaults are a good starting point. Click “Apply” to kick off
the Training. This will take from a few minutes to many days, depending
on the complexity/size of your task and the GPU hardware of your
machine.

11. During training a run TensorBoard will open and show you how your
model is performing. See here for how to interpret these plots:
https://developers.google.com/machine-learning/testing-
debugging/metrics/interpretic

12. When training completes three output files will be generated. These are
your model! You can take these anywhere to make predictions on
unlabeled data, or to use transfer learning by using these weights as initial
inputs.

13. Within Avizo you can make predictions on new data!
Start a new Project, load in the images you would like to make predictions
on, then select the “Deep Learning Prediction” module. From there select
the trained model files that were output during the training process and
click Apply.

14. This will generate a corresponding stack of image mask predictions.
Making a pixel-by-pixel prediction for which class the model assigns. You
can export this a tiff stack or whatever format for further analysis.