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All Projects → xf4j → Brats17

xf4j / Brats17

Licence: mit
Patch-based 3D U-Net for brain tumor segmentation

Programming Languages

python
139335 projects - #7 most used programming language

Labels

3dsegmentationunet

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Synopsis

Submission for Multimodal Brain Tumor Segmentation Challenge 2017 (http://braintumorsegmentation.org/). A patch-based 3D U-Net model is used. Instead of predicting the class label of the center pixel, this model predicts the class label for the entire patch. A sliding-window method is used in deployment with overlaps between patches to average the predictions.

Code Example

The workflow includes bias correction, patch extraction, training, post-processing, testing and submission. After training data is downloaded, run python bias_correction.py input_dir to perform bias field correction based on N4ITK (https://www.ncbi.nlm.nih.gov/pubmed/20378467). The corrected dataset will be saved at the same folder with the raw dataset. Run python generate_patches.py input_dir output_dir to generate patches for training. To train the model, run python main.py --train=True --train_data_dir=train_patch_dir. Or you can modify the default parameters in main.py so that you can just run python main.py. Check model.py for more details about the network structure.
To test the model on validation dataset, run python main.py --train=False --deploy_data_dir=deploy_data_dir --deploy_output_dir=deploy_output_dir. The results will be saved at deploy_output_dir. The network structure for survival prediction is not working good as the result is similar as random guessing. So you can ignore that by setting run_survival to False.
To combine the results and generate the final label maps, run python prepare_for_submission.py input_dir output_dir.

Installation

The model is implemented and tested using python 2.7 and Tensorflow 1.1.0, but python 3 and newer versions of Tensorflow should also work. Other required libraries include: numpy, h5py, skimage, transforms3d, nibabel, scipy, nipype. You also need to install ants for bias correction. Read the instructions for Nipype (http://nipy.org/nipype/0.9.2/interfaces/generated/nipype.interfaces.ants.segmentation.html) and Ants (http://stnava.github.io/ANTs/) for more information.

Contributors

Xue Feng, Department of Biomedical Engineering, University of Virginia [email protected]

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