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This work aims to explore a new approach to model the encephalic vasculature using the formalism of graphs that naturally fit the structure of blood vessels.

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EncephalicVasculatureMapping

This work aims to explore a new approach to model the encephalic vasculature using the formalism of graphs that naturally fit the structure of blood vessels.

It is necessary to install:

  • Keras with TensorFlow backend
  • h5py
  • scipy
  • sklearn
  • numpy
  • tifffile
  • argparse
  • progressbar

To obtain the graph follow the procedure below:

  1. If necessary make_train_set.py (folder: augment-train-set) performs an augmenting operation over the train-set
  2. Use train_vascular.py to train the neural network
  3. Use predict.py to segment the images
  4. It is possibile to use overlap_img.py (folder: pre-process-denoising) to overlap the predicted image (It is possibile that this operation reduces the noise)
  5. Use prepare_tif_stack.py to filter and binarize the predicted images
  6. Use the Fiji's (Fiji is just ImageJ) plug-in to create the graph that models the predicted images. To run the plug-in move in the directory containing the launcher and the run (command for linux)
    $ ./ImageJ-linux64 --ij2 --headless --run path/to/script 'img_file="path/to/image",ouput="path/to/output/folder"'
    Fiji: https://fiji.sc/#
    Headless mode: http://imagej.net/Scripting_Headless
  7. Use cluster_meanshift.py (folder: post-process-denoising) to clean the graph from noises

NB use -h to see the script's helps

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This work aims to explore a new approach to model the encephalic vasculature using the formalism of graphs that naturally fit the structure of blood vessels.

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