run_preproc_pipeline_inf2.sh

#!/bin/bash
eval "$(conda shell.bash hook)"
conda activate cta 

# define the folder containing the data
# the scans should be in a folder called "og"

# for training or evaluating on annotated data, please refer to the other pipeline file

# define the path to your data here 
export path_base="/data/aneurysm/external"

export path_og="${path_base}/og"
export path_resampled=${path_og}_0.4 
export path_vessel_seg="${path_base}/crop_0.4_vessel"
export path_crop="${path_base}/crop_0.4"
export path_edt="${path_base}/crop_0.4_vessel_edt"
export path_cvs_outputs="${path_base}/cvs_temp"
export path_cvs_masks="${path_base}/cvs_mask"
export path_cvs_bbox="${path_base}/cvs_bbox"


# Resample scans to 0.4mm spacing and crop them
python src/preprocess/resample_scans.py ${path_og} 
python src/preprocess/crop_scans.py ${path_resampled} ${path_crop}

# Run vessel segmentation
sudo docker run --gpus all -it --rm -v ${path_vessel_seg}_temp/:/Data/aneurysmDetection/output_path/  -v ${path_crop}/:/Data/aneurysmDetection/input_cta/ --shm-size=24g --ulimit memlock=-1 vessel_seg:latest python /Work/scripts/extractVessels.py -d /Data/aneurysmDetection/input_cta/ /Data/aneurysmDetection/output_path -m 'Prediction' -t 16 -s 0.5 -g 1

# # Keep only relevant files 
mkdir ${path_vessel_seg}
sudo rm  ${path_vessel_seg}_temp/Predictions/CA_*
sudo rm ${path_vessel_seg}_temp/Predictions/*.json 
cp ${path_vessel_seg}_temp/Predictions/* ${path_vessel_seg}/
sudo rm -rf ${path_vessel_seg}_temp
# Compute distance maps
python src/preprocess/compute_distance_maps.py ${path_vessel_seg} ${path_edt}

# Get cvs masks
python src/cvs_mask/compute_cvs.py ${path_crop} ${path_vessel_seg} ${path_cvs_outputs} ${path_cvs_masks} ${path_cvs_bbox} 

python src/preprocess/compute_distance_maps.py ${path_cvs_masks} ${path_cvs_masks}_edt

python src/preprocess/compress_distance_maps.py ${path_cvs_masks}_edt 128 90
python src/preprocess/compress_distance_maps.py ${path_edt} 128 90