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Copy pathrun_lidc.py
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executable file
·145 lines (117 loc) · 6.78 KB
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#!/usr/bin/env python3
import os.path as osp
from multiprocessing import freeze_support
import ruffus
import ruffus.cmdline as cmdline
import tasks
##########################################################################
# Set your environmental parameters
##########################################################################
output_dir = 'output'
data_dir = 'DATA'
temp_dir = 'tmp'
iso_size = '1'
##########################################################################
def make_pipeline_LIDC_analysis(experiment_set):
pipeline_name = experiment_set
data_path = osp.join(data_dir, experiment_set)
output_path = osp.join(output_dir, experiment_set)
feature_list_path = osp.join(
output_dir, "feature-list_" + experiment_set + ".csv")
pipeline = ruffus.Pipeline(pipeline_name)
starting_file_names = list(tasks.load_patients_list(data_path))
#print(starting_file_names)
pipeline.originate(name="task_originate",
task_func=tasks.originate,
output=starting_file_names) \
.follows(ruffus.mkdir(output_path)) \
.follows(ruffus.mkdir(data_path)) \
.follows(ruffus.mkdir(data_path+"/CT"))
pipeline.transform(name="task_image_resample",
task_func=tasks.image_resample,
input=ruffus.output_from("task_originate"),
filter=ruffus.formatter(),
output="{path[0]}/{basename[0]}-" + iso_size + "mm.nrrd")
pipeline.subdivide(name="task_check_nodules",
task_func=tasks.check_nodules,
input=ruffus.output_from("task_originate"),
filter=ruffus.formatter(
experiment_set + r"/CT/(?P<pid>[^/]+)_CT.nrrd"),
output=["{subpath[0][1]}/{pid[0]}/{basename[0]}_*.nodule"])
pipeline.transform(name="task_extract_nodule_labels",
task_func=tasks.extract_nodule_labels,
input=ruffus.output_from("task_check_nodules"),
filter=ruffus.formatter(),
output=["{path[0]}/{basename[0]}-Phy1-label.nrrd",
"{path[0]}/{basename[0]}-Phy2-label.nrrd",
"{path[0]}/{basename[0]}-Phy3-label.nrrd",
"{path[0]}/{basename[0]}-Phy4-label.nrrd"])
pipeline.subdivide(name="task_feature_extraction_phy",
task_func=tasks.feature_extraction_phy,
input=ruffus.output_from("task_extract_nodule_labels"),
filter=ruffus.formatter(
experiment_set + r"/(?P<pid>[^/]+)/(?P<image_name>.*)_(?P<nid>\d*)-.*-label.nrrd"),
output=[output_dir + "/" + experiment_set + "/{image_name[0]}_{nid[0]}-Phy1-" + iso_size + "mm.txt",
output_dir + "/" + experiment_set + "/{image_name[0]}_{nid[0]}-Phy2-" + iso_size + "mm.txt",
output_dir + "/" + experiment_set + "/{image_name[0]}_{nid[0]}-Phy3-" + iso_size + "mm.txt",
output_dir + "/" + experiment_set + "/{image_name[0]}_{nid[0]}-Phy4-" + iso_size + "mm.txt"],
extras=["{subpath[0][1]}/CT/{image_name[0]}.nrrd"])\
.follows("task_image_resample")
pipeline.transform(name="task_staple_comparison",
task_func=tasks.staple_comparison,
input=ruffus.output_from("task_extract_nodule_labels"),
filter=ruffus.formatter(
experiment_set + r"/(?P<pid>[^/]+)/(?P<image_name>.*)_(?P<nid>\d*)-.*-label.nrrd"),
output=["{path[0]}/{image_name[0]}_{nid[0]}-all-label.nrrd",
"{path[0]}/{image_name[0]}_{nid[0]}-STAPLE-label.nrrd",
output_dir + "/" + experiment_set + "/{image_name[0]}_{nid[0]}-STAPLE.txt"])
pipeline.transform(name="task_segment_nodule",
task_func=tasks.segment_nodule,
input=ruffus.output_from("task_staple_comparison"),
filter=ruffus.formatter(
experiment_set + r"/(?P<pid>[^/]+)/(?P<image_name>.*)_(?P<nid>\d*)-all-label.nrrd"),
output=["{path[0]}/{image_name[0]}_{nid[0]}-seg-label.nrrd",
"{path[0]}/{image_name[0]}_{nid[0]}-levelset.nrrd"],
extras=["{subpath[0][1]}/CT/{image_name[0]}.nrrd"])\
pipeline.transform(name="task_feature_extraction_seg",
task_func=tasks.feature_extraction,
input=ruffus.output_from("task_segment_nodule"),
filter=ruffus.formatter(
experiment_set + r"/(?P<pid>[^/]+)/(?P<image_name>.*)_(?P<nid>\d*)-seg-label.nrrd"),
output=output_dir + "/" + experiment_set +
"/{image_name[0]}_{nid[0]}-seg-" + iso_size + "mm.txt",
extras=["{subpath[0][1]}/CT/{image_name[0]}.nrrd"])\
.follows("task_image_resample")
pipeline.merge(name="task_staple_organization",
task_func=tasks.staple_organization,
input=ruffus.output_from("task_staple_comparison"),
output=output_dir + "/STAPLE_" + experiment_set + ".csv")
pipeline.transform(name="task_feature_extraction",
task_func=tasks.feature_extraction,
input=ruffus.output_from("task_staple_comparison"),
filter=ruffus.formatter(
experiment_set + r"/(?P<pid>[^/]+)/(?P<image_name>.*)_(?P<nid>\d*)-all-label.nrrd"),
output=output_dir + "/" + experiment_set +
"/{image_name[0]}_{nid[0]}-all-" + iso_size + "mm.txt",
extras=["{subpath[0][1]}/CT/{image_name[0]}.nrrd"])\
.follows("task_image_resample")
pipeline.merge(name="task_feature_organization",
task_func=tasks.feature_organization,
input=ruffus.output_from(
["task_feature_extraction", "task_feature_extraction_phy", "task_feature_extraction_seg"]),
output=feature_list_path)
return pipeline
if __name__ == "__main__":
freeze_support()
experiment_set = 'nodule-lidc'
pipeline_LIDC = make_pipeline_LIDC_analysis(experiment_set)
parser = cmdline.get_argparse(description='LIDC radiomics')
options = parser.parse_args()
# standard python logger which can be synchronised across concurrent
# Ruffus tasks
logger, logger_mutex = cmdline.setup_logging(
__name__, options.log_file, options.verbose)
#
# Run
#
cmdline.run(options)