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Abstract:
BraTS Toolkit is a holistic approach to brain tumor segmentation and consists out of out of three components: First, the BraTS Preprocessor facilitates data standardization and preprocessing for researchers and clinicians alike. It covers the entire image analysis workflow prior to tumor segmentation, from image conversion and registration to brain extraction. Second, BraTS Segmentor enables orchestration of BraTS brain tumor segmentation algorithms for generation of fully-automated segmentations. Finally, BraTS Fusionator can combine the resulting candidate segmentations into consensus segmentations using fusion methods such as majority voting and iterative SIMPLE fusion. The capabilities of our tools are illustrated with a practical example to enable easy translation to clinical and scientific practice.
To install the most up to date version from the master branch please use the following pip install command:
pip install -e git+https://github.com/neuronflow/BraTS-Toolkit-Source.git@master#egg=brats_toolkit
If you use BraTS Toolkit please cite:
Kofler, F., Berger, C., Waldmannstetter, D., Lipkova, J., Ezhov, I., Tetteh, G., Kirschke, J., Zimmer, C., Wiestler, B., & Menze, B. H. (2020). BraTS Toolkit: Translating BraTS Brain Tumor Segmentation Algorithms Into Clinical and Scientific Practice. Frontiers in neuroscience, 14, 125. https://doi.org/10.3389/fnins.2020.00125
Please also cite the following original authors of the algorithms who make this repository and tool possible:
| Docker | Paper |
|---|---|
| econib | Marcinkiewicz, M., Nalepa, J., Lorenzo, P., Dudzik, W., & Mrukwa, G. (2018). Segmenting brain tumors from MRI using cascaded multi-modal U-Nets. In International MICCAI Brainlesion Workshop (pp. 13–24). |
| mic-dkfz | Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. (2018). No new-net. In International MICCAI Brainlesion Workshop (pp. 234–244). |
| scan | McKinley, R., Meier, R., & Wiest, R. (2018). Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In International MICCAI Brainlesion Workshop (pp. 456–465). |
| xfeng | Feng, X., Tustison, N., & Meyer, C. (2019). Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 279–288). Springer International Publishing. |
| lfb_rwth | Weninger, L., Rippel, O., Koppers, S., & Merhof, D. (2019). Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challenge. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 3–12). Springer International Publishing. |
| gbmnet | Nuechterlein, N., & Mehta, S. (2019). 3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 245–253). Springer International Publishing. |
| xyz_2019 | Zhao, Y.X., Zhang, Y.M., Song, M., & Liu, C.L. (2019). Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (pp. 256–265). Springer International Publishing. |
| scan_2019 | McKinley, R., Rebsamen, M., Meier, R., & Wiest, R. (2020). Triplanar Ensemble of 3D-to-2D CNNs with Label-Uncertainty for Brain Tumor Segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 379–387). Springer International Publishing. |
BraTS Preprocessor facilitates data standardization and preprocessing for researchers and clinicians alike. It covers the entire image analysis workflow prior to tumor segmentation, from image conversion and registration to brain extraction.
BraTS Preprocessor offers the following preprocessing modes:
Activates HD-BET in CUDA mode. Only supported when using NVIDIA docker (Linux operating systems only for the moment).
Activates HD-BET or ROBEX in CPU mode depending on available RAM.
Activates Robex brain extraction.
Activates GPU defacing - not implemented yet falls back to cpu_defacing for the moment.
Activates mri-deface to deface using CPU. Use this mode for further processing on the Kraken (https://neuronflow.github.io/kraken/)
BraTS preprocessor offers processing files in batch or single mode. Please have a look at the respective example scripts / CLI commands for information how to use them.
For faster computation we strictly recommend the batch processing mode, which avoids additional overhead of spawning and shutting down multiple docker containers and instead does all the processing in one container.
You can find instructions to use and download the GUI variant here: https://neuronflow.github.io/BraTS-Preprocessor/
Please have a look at 0_preprocessing_batch.py and 0_preprocessing_single.py in this repository for a demo application.
Type brats-preprocess -h after installing the python package to see available options.
Type brats-batch-preprocess -h after installing the python package to see available options.
BraTS Segmentor enables orchestration of BraTS brain tumor segmentation algorithms for generation of fully-automated segmentations.
Please have a look at 1_segmentation.py in this repository for a demo application.
Type brats-segment -h after installing the python package to see available options.
BraTS Fusionator can combine the resulting candidate segmentations into consensus segmentations using fusion methods such as majority voting and iterative SIMPLE fusion.
Please have a look at 2_fusion.py in this repository for a demo application.
Type brats-fuse -h after installing the python package to see available options.
The source code for BraTS Toolkit can be found here: https://github.com/neuronflow/BraTS-Toolkit-Source
Open an issue in this git repository or contact us per email.
Florian Kofler florian.kofler [at] tum.de
Christoph Berger c.berger [at] tum.de
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