SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI

Development framework for evaluation of deep learning architectures in the paper (https://arxiv.org/abs/1810.13304)

The method makes use of Keras and Tensorflow. If the method is running on GPU, please make sure CUDA 9.X is correctly installed. Then, in the base directory run:

pip install -r requirements.txt

1. Read ISLES challenge registration instructions in the 'How to join' section and register.

2. Download and extract the ISLES2015 (SISS and SPES) and ISLES2017 datasets.

3. Update the dataset dictionary with the path to each dataset in configuration.py (line 137).

4. Reproduce the cross-validation results in the paper by running :

   python main.py
   

   For each performed cross-validation:

   1. The included pre-trained models from checkpoints/ will be loaded for the corresponding fold.

   2. The corresponding validation images of the training set will be segmented.

   3. Finally, the computed evaluation metrics will be written to a spreadsheet file.

5. Accessing the results:

   • The resulting binary segmentations will be found in the results/ folder.
   • A spreadsheet with the evaluation metrics for each crossvalidation will be in the metrics/ folder.