joseabernal / iSeg2017-nic_vicorob

Implementation of the nic_vicorob team for addressing the MICCAI Grand Challenge on 6-month infant brain MRI segmentation iSeg2017

Six-month infant brain tissue segmentation using three dimensional fully convolutional neural networks and pseudo-labelling

Implementation of the nic_vicorob team for addressing the MICCAI Grand Challenge on 6-month infant brain MRI segmentation iSeg2017.

Requirements

Folder structure

Once the repository has been clone/downloaded, there will be only the log and models folders and the iSeg2017.ipynb file. Add the folders datasets (folder containing the testing and training sets provided by the challenge organisers), results and refined-results. The resulting tree should look as indicated below.

.
├── datasets
│   └── iSeg2017
│       ├── iSeg-2017-Testing
│       └── iSeg-2017-Training
├── iSeg2017.ipynb
├── log
│   └── iSeg2017
├── models
│   └── iSeg2017
├── refined-results
│   └── iSeg2017
│       └── iSeg-2017-Testing
└── results
    └── iSeg2017
        └── iSeg-2017-Testing 

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

Once all the libraries above have been installed, the following step is to run the jupyter notebook on the folder containing the iSeg2017.ipynb file.

jupyter notebook