This repository contains scripts for deep learning in guided medical interventions. The complete workflow of data processing is implemented, from formatting and annotations to deployment of models in real time.

• Install Anaconda (with Python 3.7)
• Run the setup_env.bat file (in SetupAnaconda folder) to create environment in a folder.
• Clone this repository on your computer.
• Some notebooks will require that you createa a new file in the Notebooks folder of your local clone, named local_vars.py, and define the root_folder variable in that file. The file can just contain this single line of code: root_folder = r"c:\Data". Please do not commit/push your local_vars.py, as everybody sets it up with their own local paths!

• Install Slicer 4.11 or newer version (later than 2019-09-16 is recommended, for full functionality)
• Install the Jupyter extension for Slicer, and follow the extension user guide to add Slicer as a kernel in Jupyter Notebook.
• Install additional packages in the Slicer python environment, to be able to run all Slicer notebooks. Use the Python console of Slicer to run this command (you may add additional python libraries that you use in your code):

pip_install("tensorflow keras scikit-learn opencv-contrib-python")

• You may also install tensorflow-gpu, if your computer supports that. In that case copy CUDA files (dll files on Windows) to the Slicer bin folder. (Feel free to add instructions for Mac and Linux.)
• To run notebooks, start the Anaconda command prompt, navigate to the Notebooks folder of your clone of this repository, and type the jupyter notebook command.

• Use the Sequences extension in 3D Slicer to record tracked ultrasound sequences.
• Record Image_Image (the ultrasound in the Image coordinate system) and ImageToReference transform sequences. Note that Slicer cannot record transformed images, so recording Image_Reference is not an option.
• If you work on segmentation, you can use the SingleSliceSegmentation Slicer module in this repository to speed up manual segmentation.
• Create annotations by placing fiducials or creating segmentations with the SingleSliceSegmentation module.
• For segmentations, you may use the SingleSliceSegmentation module to export images and segmentations in png file format.
• Use scripts in the Notebooks/Slicer folder to export images and annotations.

• It is recommended that you save separate sequences for validation and testing
• Use Notebooks/FoldersToSavedArrays to save data sequences as single files for faster data loading during training.