NoduleX_Docker

Containerize the process from https://github.com/jcausey-astate/NoduleX_code

Step 1: Build dataset

will create dir in /data/ called "built_dataset"
/NoduleX_Docker/dicom_and_image_tools/simplify-doi-structure.sh -s /NoduleX_Docker/data/DOI /NoduleX_Docker/data/built_dataset
"DOI" contains the proginal data downloaded from https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
"built_dataset" contains dataset built to a flattened directory structure

Step 2: Create Segmentation Masks(DICOM format)

(temporarily upload zip file from host to container)
will create dir in /data/ called "binary_dicom"
process in batch: create_seg_masks.sh
'''
for n in ls -d data/built_dataset/* ; do
echo "Converting nodule $n" ;
python dicom_and_image_tools/segment_to_binary_image.py --candidates data/nodule_lists/S1vS45_TRAIN_candidates.txt --segmented-only
"$n"
"data/binary_dicom/$(basename $n)"
&& echo "OK"
|| echo "FAILED converting nodule $n"
;done
’‘’
To build the docker image, run
docker build -t segmask .

Step 3: Convert DICOM to Analyze format

will create dir in /data/ called "binary_analyze"
run: dicom_to_analyze.sh
'''
p=LIDC-IDRI-0011;
for n in ls -d data/binary_dicom/$p/* ; do
echo "Converting nodule $n" ;
python dicom_and_image_tools/dicom_to_analyze.py
"$n"
"data/binary_analyze/$p/$(basename $n)"
&& echo "OK"
|| echo "FAILED converting nodule $n"
;done
'''
To build docker image, run
docker build -t analyze .

Step 4: QIF feature extraction

This part involves dockerizing Feature Extraction process.
TODO: wirte a script to change directory structure and naming convention in order to run the code properly.
Docker image already built, run
docker build -t octave .