UsAnnotationExport

Notebooks for 3D Slicer to export ultrasound annotations for machine learning. Notebooks also contain scripts for processing exported data, and some example deep learning methods.

Getting started

Install and set up Anaconda environment

• 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, e.g. root_folder = r"c:\Data". Please do not commit/push your local_vars.py!

To run Slicer notebooks

• Install Slicer 4.11 or newer version (after 2019-05-31, for full functionality)
• Install the Jupyter extension for Slicer, and follow the extension user guide to add the Python environment of Slicer as a kernel in Jupyter Notebook.
• Install additional packages in the Slicer python environment, to be able to run all Slicer notebooks. Open a cmd terminal as Administrator, go the Slicer bin folder, and use this command PythonSlicer.exe -m pip install tensorflow keras scikit-learn ipywidgets
• 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. Note: Some Slicer notebooks are written for Slicer 4.10 (Python 2.7). They will be gradually updated for Slicer 4.11 (Python 3.6).

Acquire tracked ultrasound

• 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.
• Create annotations by placing fiducials or creating segmentations.
• Save the Slicer scene.
• Use Notebooks/Slicer/AverageIntensities to determine intensity threshold and image region for settinput up filter in later notebooks to skip images with no skin contact.
• Use scripts in the Notebooks/Slicer folder to export images and annotations. These scripts will automatically open a Slicer managed by Jupyter. Load the saved Slicer scenes in these managed Slicer instances and run the notebooks to export data.

Process exported data

• (optional) Use Notebooks/SplitAnnotatedData to separate part of the data into testing, validation, and training folders.
• Use Notebooks/FoldersToSavedArrays to save data sequences as single files for faster data loading during training.