Note: Shortly this setup guide will be replaced with a setup.py
The experiments for the paper were run on a microsoft azure vm (https://azuremarketplace.microsoft.com/en-gb/marketplace/apps/microsoft-dsvm.linux-data-science-vm-ubuntu) with NC6 Promo. Once 'ssh'ed into the vm the following tasks were performed:
- Clone this repository
- We used conda to install the packages required for this project:
conda create -n spine-env
source activate spine-env
conda install tensorflow-gpu==1.12.0
conda install keras
conda install matplotlib
conda install -c https://conda.anaconda.org/simpleitk SimpleITK
conda install pip
pip install keras-metrics
pip install elasticdeform
To reproduce the results of the paper follow these instructions:
- First you must download the data from BioMedia: https://biomedia.doc.ic.ac.uk/data/spine/. In the dropbox package there are collections of spine scans called 'spine-1', 'spine-2', 'spine-3', 'spine-4' and 'spine-5', download and unzip these files and move all these scans into a directory called 'training_dataset'. You will also see a zip file called 'spine-test-data', download and unzip this file and rename it 'testing_dataset'.
- You must then generate samples to train and test the detection network.
python generate_detection_samples.py 'training_dataset' 'samples/detection/training'python generate_detection_samples.py 'testing_dataset' 'samples/detection/testing' - Now train the detection network:
python train_detection_model.py 'samples/detection/training' 'samples/detection/testing' 'saved_models/detection.h5' - You must then generate samples to train and test the identification network.
python generate_identification_samples.py 'training_dataset' 'samples/identification/training'python generate_identification_samples.py 'testing_dataset' 'samples/identification/testing' - Now train the identification network:
python train_identification_model.py 'samples/identification/training' 'samples/identification/testing' 'saved_models/identification.h5' - You can now run the full algorithm on the test data. It should be noted that due to the randomness of sample generation
and the stochastic nature of training a network the results may not be exactly as stated in the paper (could be higher or lower).
To get results for the same metrics run
python measure.py 'testing_dataset' 'saved_models/detection.h5' 'saved_models/identification.h5'