Deepmedic and 3D U-Net for brain tumor segmentation
We created two popular deep learning models DeepMedic and 3D U-Net in PyTorch for the purpose of brain tumor segmentation. The performance of our proposed ensemble on BraTS 2018 dataset is shown in the following table:
| Dataset | Dice(ET) | Dice(WT) | Dice(TC) | Hausdorff95(ET) | Hausdorff95(WT) | Hausdorff95(TC) |
|---|---|---|---|---|---|---|
| Testing | 0.782 | 0.908 | 0.823 | 2.96 | 4.39 | 6.91 |
For more details about our methodology, please refer to our paper ET:Enhancing tumor, WT: whole tumor, TC: tumor core.The final trained model for multiclass classification is included along with the results on test dataset.
The system was employed for our research presented in [1,2], where the we integrate multiple DeepMedics and 3D U-Nets in order to get a robust tumor segmentation mask. We also utilize the brain parcellation masks for the purpose of bringing the location information to DeepMedic and 3D U-Net. If the use of the software or the idea of the paper positively influences your endeavours, please cite [1,2].
[1] Po-Yu Kao, Thuyen Ngo, Angela Zhang, Jefferson Chen, and B. S. Manjunath, "Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction." International MICCAI Brainlesion Workshop. Springer, Cham, 2018.
[2] Po-Yu Kao, Shailja Shailja, Jiaxiang Jiang, Angela Zhang, Amil Khan, Jefferson W. Chen, and B. S. Manjunath, "Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information" Front. Neurosci. 13:1449. doi: 10.3389/fnins.2019.01449
Python3.6 xgboost