Segment hematoma slice-wise from CT images with traumatic brain injury.

Create data directories and subdirectories as below. Training will be executed over the data in the train directory and tested over data in the test directory.

The my_images_to_segment directory is for images we don't have the masks for

./tbi_ct_lesion_segmentation/
+-- data/
|   +-- train/
|   |   +-- file_1_CT.nii.gz
|   |   +-- file_1_mask.nii.gz
|   |   +-- file_2_CT.nii.gz
|   |   +-- file_2_mask.nii.gz
|   |   +-- file_3_CT.nii.gz
|   |   +-- file_3_mask.nii.gz
|   +-- test/
|   |   +-- file_1_CT.nii.gz
|   |   +-- file_1_mask.nii.gz
|   |   +-- file_2_CT.nii.gz
|   |   +-- file_2_mask.nii.gz
|   |   +-- file_3_CT.nii.gz
|   |   +-- file_3_mask.nii.gz
|   +-- my_images_to_segment/
|   |   +-- file_1.nii.gz
|   |   +-- file_2.nii.gz
|   |   +-- file_3.nii.gz
|   |   +-- file_n.nii.gz

Run train.py to train a classification model with some desired arguments.

--datadir: Path to where the unprocessed data is

--psize: Size of patches to gather, of the from INTEGERxINTEGER. INTEGER should be a power of 2

--num_patches: Number of patches to extract from each CT volume

--batch_size: Batch size for training the neural network

--num_channels: Number of channels in training images.

--experiment_details: A string describing this training run, for human organization

--gpuid: the ID of the GPU to train with, for multi-GPU systems. Enter -1 to utilize all available GPUs

Example usage: python train.py --traindir data/train/ --psize 128x128 --num_patches 1000 --batch_size 128 --num_channels 1 --experiment_details my_experiment

Run segment.py to classify a single image with some desired arguments:

--infile: Path to where the unprocessed single image volume is

--inmask: (OPTIONAL) Path to where the manually created binary mask volume is

--weights: path to the trained model weights (.hdf5) to use

--segdir: Path to directory in which segmentations will be placed.

Example usage: python validate.py --infile my_data/my_first_image.nii.gz --inmask mydata/my_first_image_mask.nii.gz --weights my_weighs.hdf5 --segdir my_segmentation_dir

Example usage if manual mask is unavailable: python validate.py --infile my_data/my_first_image.nii.gz --weights my_weighs.hdf5 --segdir my_segmentation_dir

Run test.py to validate the model on some holdout data for which the ground truth is known and record metrics with some desired arguments:

--datadir: Path to where the unprocessed data is

--weights: path to the trained model weights (.hdf5) to use

Example usage: python validate.py --datadir data/test/ --weights models/weights/my_experiment/my_weights.hdf5

Here are all the preprocessing steps which are automatically executed in train.py, validate.py, and test.py.

All preprocessing code is located in utils/utils.py.

1. Skullstrip according to CT_BET.sh
2. Orient to RAI

These preprocessing steps require the following external programs:

• fslmaths (included in FSL)
• bet2 (included in FSL)
• 3dresample (included in AFNI)
• 3dinfo (included in AFNI)

{TODO}