3. Denoising with Noise2Void

Disclaimer: Noise2Void improves the visual quality of an image. However, it is uncertain whether or not signals remain scientifically accurate for quantification (morphological quantification may be fine, intensities not)

Useful for:

Visualization
Intermediary step for mask generation
Segmentation

Credits:

1. Start Noise2Void with the icon on the desktop : N2V

2. Start by entering your credentials for the server omero-cai.hhu.de and click on "Connect"

4. You can then select the group in which the dataset you want to process is located, and indicated the dataset ID. Load the dataset by pressing "Load dataset". You can change which image ID, Z, T or C to display by updating the field and clicking on "Set"

5. To train the network to denoise your images, go to the tab "Train N2V"

Patch size: Together with "Batch size", will change how much data is passed to the GPU per batch. A patch of 256x256 with a batch size of 16 works best for our workstation (the one next to the spinning disk microscope)
Number of training epochs: Increase this if your loss is still decreasing after 100 epochs.
Steps per epochs: You can increase this parameter if you want your network to train on a larger portion of data for each epoch.
Train fraction: Indicates which portion of the dataset is used for training, the rest being used for validation. Increase this to 0.99 if you have a large dataset and you want to avoid the validation step to take too long (there is one validation step per epoch)
Neighborhood radius: N2V parameter. Read more about it in their paper https://arxiv.org/abs/1811.10980

4. Apply a trained model to a dataset by going to "Apply N2V"

By default, the program will upload the images in the same dataset by adding the suffix "_N2V" to the image name
Images can be uploaded during the processing. Otherwise, they can be uploaded when the processing is done with "Upload to omero"
Result display can be toggled off to make the process faster.

5. Apply the model to the dataset and upload results to OMERO → Start with this step if you already have a trained model for your images