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+# FLAIR abnormality segmentation in brain MRI
+
+This folder contains an implementation of a FLAIR abnormality segmentation in brain MRI using U-Net like architecture.
+It can be used to preprocess MRI images, train the network for tumor segmentation or obtain segmentation predictions of  FLAIR abnormality in a custom dataset (not expected to work outstandingly well without prior fine-tuning).
+
+### Results
+The results presented here refer to the network fine-tuned for 64 epochs and initialized with the weights for skull stripping task based on FLAIR modality only.
+This training procedure requires to have the same training/test split for both FLAIR segmentaiton and skull stripping tasks.
+It was not used in the application described in our paper.
+Still, it makes the developed architecture, trained weights, and code more widely applicable.
+
+Some qualitative results for the worst (80.74% DSC) and best case (92.37% DSC) from the test set before postprocessing.
+Blue outline corresponds to ground truth and red to automatic segmentation output.
+Images show FLAIR modality after preprocessing and skull stripping.
+
+| Worst Case | Best Case |
+|:----------:|:---------:|
+|![Worst case](DU_7306.gif)|![Best case](DU_5851.gif)|
+
+The average Dice similarity coefficient (DSC) for this split was 88.37%.
+The distribution of DCS is shown below.
+
+![DCS](DSC.png)
+
+Training log for a random 5 test cases split (the same as in skull stripping):
+
+![training](training.png)
+
+### Usage
+
+#### Preprocessing
+You need to have a folder with images preprocessed using provided matlab function `preprocessing3D.m`.
+It rescales them to have spacial dimensions 256x256 and performs contrast normalization.
+The preprocessing is done for a 3D volume of slices yet for each MRI modality separately.
+Refer to the documentation of `preprocessing3D.m` function for more details.
+The main requirement for the following steps is to have image names in format `<case_id>_<slice_number>.tif` and corresponding masks named `<case_id>_<slice_number>_mask.tif`.
+
+#### Training
+The training script `train.py` has variables defined at the top that you need to set:
+
+- `train_images_path` - folder containing training images
+- `valid_images_path` - folder containing validation images
+    
+Other variables can be changed to adjust some training parameters.
+One of them, `init_weights_path`, allows to initialize the network with pre-trained weights, e.g. provided by us or from the model trained for skull stripping (in this case make sure to have the same training/test splits for both tasks).
+The models described in our paper were trained from scratch.
+
+Then, run the training using
+```
+python train.py
+```
+
+#### Testing
+To run the inference, you need to set up some variables defined at the top of the `test.py` script:
+
+- `weights_path` - path to the trained weights
+- `train_images_path` - folder containing training images to compute the mean and standard deviation for data normalization; if you pass your own mean and standard deviation to the `test` function, this variable is not used
+- `test_images_path` - folder with test images for prediction; it must contain corresponding mask files as well, however, they can be dummy (all zeros)
+- `predictions_path` - folder for saving predicted and ground truth segmentation outlines (will be created if it doesn't exist)
+
+When all variables are set up, run the inference using
+```
+python test.py
+```
+
+If you want to use our trained weights for inference, you should use mean and standard deviation values for normalization computed on our training set.
+They are the default parameter values used in the `test` function of `test.py` script.
+
+Trained weights can be downloaded using provided script
+```
+./download_weights.sh 
+```
+
+### Troubleshooting
+
+#### AttributeError: 'module' object has no attribute 'rolling_count'
+
+Try updating dask (related issue: [tflearn/issues/766](https://github.com/tflearn/tflearn/issues/766)):
+
+```
+sudo pip install dask --upgrade
+```