nic-vicorob / tissue_segmentation_comparison

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.

Requirements

Libraries

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

How to run it

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