To Run the Network on Test Data:

-Step 1: Running the Network to Detect the ON and OFF surfaces

• First, put all the images to be processed (STD Tiff stacks of chAT Bands) into the ImagesHere folder.

• Then, open Matlab, make sure Matlab has included all the folders and subfolders inside of VNet, and run the script RunMe.m

• After the Matlab script has finished running, the 2 chAT surfaces will be stored in SurfacesDectected Folder, plus a Tiff file containing the ON and OFF surfaces detected by the Network overlay on top of the Original Tiff file.

• Manually verify that the overlays are correct by looking at the Tiff files mentioned above. If yes, import the 2 chAT surfaces, stored as filename_ON.mat and filename_OFF.mat into Sumbul RGC code to continue with the Warping step. These two .mat files contain the 2 surfaces that would have been returned by Sumbul's firSurfaceToSACAnnotation function.

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-Step 2: Clean Up

• Make sure to delete or remove the images in ImageHere so that you can use the network again with other images.

• Delete all the Detected Surfaces in DetectedSurfaces that are no longer needed also.

• Delete all the Tiff files from ResultsON and ResultsOFF folder as well, if no longer needed,

######################################################################################################## Notes: In the case where the overlays are not correct, manually annotated the data, and store the Annotated files (xls or txt format) in the VNet/Dataset/Annotations folder so that the network can train itself with the new data. ########################################################################################################

To Train the Network with new Data:

-Step 1: Install Dependencies and Libraries

• Run CaffeInstallation.sh and PythonLibraryInstallation.sh. To run the 2 files, either double-click on the file name and choose 'Run On Terminal', or manually open up a new Terminal, change directory (cd command) to locate the file, and type: ./filename.sh in order to execute the command that download and install dependencies necessary to run pycaffe.

-Step 2: Prepare training data

• The ON and OFF band should be trained on separated network for best result, and all the images has to be resized to 128x128x64 before feeding into the network.

• First, put all the raw images that needed to be train in the Dataset/RawImages folder.

• Then, put the corresponding ON and OFF .txt annotation files into the Dataset/Annotations Folder. Note that the annotation files should be in .txt format.

• Find the CreateTrainingData.m script and run it on Matlab to create the Groundtruths, resize the images and the groundtruths, and put them in the right location ready to be trained.

-Step 3: Run the training script

• Open a Terminal, locate the VNet Directory, and type: python main.py -trainON to run and train the network with the ON chATband data. This should take 2-3 days depending on the number of iterations specified in main.py (default is 100,000).

• After the network has completed training the ON data, open a new Terminal, locate the VNet Directory, and type: python main.py -trainOFF to run and train the network with the OFF chATband data. This should also take 2-3 days depending on the number of iterations specified in main.py (default is 100,000).

• The resulting Trained Models should be in Models/SnapshotsOFF for OFF data and Models/SnapshotsON for ON data.

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VNet

Reference: https://sagarhukkire.github.io/Vnet-Cafffe_Guide/