Code corresponding to the paper "Deep Learning Methods Allow Fully Automated Segmentation of Metacarpal Bones to Quantify Volumetric Bone Mineral Density"
This repository contains the necessary parts to recreate the results of our paper.
The segmentation prediction on the right was produced by our method. On the left is the corresponding ground-truth manual segmentation.
The "deep_learning" folder contains all pieces necessary to train the networks mentioned in the paper In "data_management" the components to interact with the propriatary file formats from the HR-pQCT scanner as well as dataset handling are included.
We recommend using conda for the management of installed packages. To install the necessary packages run the following command
conda env create -f environment.yml
To train a network, use the file "ct_model.py" in the "deep_learning" folder.
python deep_learning/ct_model.py
To validate the trained models on full resolution images, please use the "validate_full_res.py" file in the "deep_learning" folder.
python deep_learning/validate_full_res.py
To use the trained models in a production environment please use "inference.py" in the "deep_learning" folder.
python deep_learning/inference.py