Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging
This repository implements the evaluation framework proposed in one of our research papers:
Bernal, J., Kushibar, K., Cabezas, M., Valverde, S., Oliver, A., Lladó, X. (2017). "Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging." IEEE Access 7 (2019): 89986-90002.
The code has been tested with the following configuration
- h5py == 2.7.0
- ipython == 5.3.0
- jupyter == 1.0.0
- keras == 2.0.2
- nibabel == 2.1.0
- nipype == 0.12.1
- python == 2.7.12
- scipy == 0.19.0
- sckit-image == 0.13.0
- sckit-learn == 0.18.1
- tensorflow == 1.0.1
- tensorflow-gpu == 1.0.1
There are two main steps to run our framework. First, update parameters inside the configuration.py file. Make sure you update dataset_info inside general_configuration to your specific setup. Also, update fields on training_configuration to desired values. Approaches that can be tried are 'DolzMulti', 'Kamnitsas', 'Guerrero' and 'Cicek'. Second, run the following on command line
python main.py