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)
Installation
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
Running the code
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Read ISLES challenge registration instructions in the 'How to join' section and register.
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Download and extract the ISLES2015 (SISS and SPES) and ISLES2017 datasets.
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Update the dataset dictionary with the path to each dataset in
configuration.py
(line 137). -
Reproduce the cross-validation results in the paper by running :
python main.py
For each performed cross-validation:
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The included pre-trained models from
checkpoints/
will be loaded for the corresponding fold. -
The corresponding validation images of the training set will be segmented.
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Finally, the computed evaluation metrics will be written to a spreadsheet file.
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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.
- The resulting binary segmentations will be found in the