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PrepareDataset.py
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PrepareDataset.py
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import os, argparse, sys, csv, platform, subprocess, shutil, posixpath, yaml
from typing import Union
from pathlib import Path
from datetime import date
import pandas as pd
import SimpleITK as sitk
from tqdm import tqdm
import numpy as np
from skimage.measure import label
from copy import deepcopy
from FigureGenerator.screenshot_maker import figure_generator
from GANDLF.cli import main_run
from LabelFusion.wrapper import fuse_images
from .constants import *
def setup_parser():
copyrightMessage = (
"Contact: admin@fets.ai\n\n"
+ "This program is NOT FDA/CE approved and NOT intended for clinical use.\nCopyright (c) "
+ str(date.today().year)
+ " University of Pennsylvania. All rights reserved."
)
parser = argparse.ArgumentParser(
prog="PrepareDataset",
formatter_class=argparse.RawTextHelpFormatter,
description="This application calls the BraTSPipeline for all input images and stores the final and intermediate files separately.\n\n"
+ copyrightMessage,
)
parser.add_argument(
"-inputCSV",
type=str,
help="The absolute path of the input CSV file containing the list of subjects and their corresponding images",
required=True,
)
parser.add_argument(
"-outputDir",
type=str,
help="The output dir to write the results",
required=True,
)
parser.add_argument(
"-executablePath",
type=str,
help="The path to the BraTSPipeline executable. If not given, will infer from current script's location.",
nargs="?",
const=None,
)
return parser
def _get_relevant_dicom_tags(filename: str) -> dict:
"""
This function reads the relevant DICOM tags from the input DICOM directory.
Args:
filename (str): The input DICOM filename.
Returns:
dict: The relevant DICOM tags.
"""
input_dicom_dir = filename
if os.path.isfile(filename):
input_dicom_dir = os.path.dirname(filename)
output_dict = {}
try:
series_IDs = sitk.ImageSeriesReader.GetGDCMSeriesIDs(input_dicom_dir)
# if len(series_IDs) > 1:
# print(
# f"WARNING: Multiple series IDs detected in {input_dicom_dir}.",
# file=sys.stderr,
# )
series_file_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(
input_dicom_dir, series_IDs[0]
)
series_reader = sitk.ImageSeriesReader()
series_reader.SetFileNames(series_file_names)
series_reader.MetaDataDictionaryArrayUpdateOn()
series_reader.LoadPrivateTagsOn()
itk_image = series_reader.Execute()
output_dict = {
"Resolution": str(itk_image.GetSpacing()).replace(" ", ""),
}
# although _technically_ the metadata is different for each slice, we'll just use the first slice's metadata, since the rest is not relevant for our purposes
## reference: https://simpleitk.readthedocs.io/en/master/link_DicomSeriesReadModifyWrite_docs.html
keys_to_extract = {
"0008|0008": "Image Type",
"0008|0070": "Manufacturer",
"0008|1090": "Manufacturer's Model Name",
"0008|0022": "Acquisition Date",
"0008|0032": "Acquisition Time",
"0010|1010": "Patient's Age",
"0010|0040": "Patient's Sex",
"0018|0020": "Scanning Sequence",
"0018|0021": "Sequence Variant",
"0018|0022": "Scan Options",
"0018|0023": "MR Acquisition Type",
"0018|0080": "Repetition Time",
"0018|0081": "Echo Time",
"0018|0082": "Inversion Time",
"0018|1310": "Acquisition Matrix",
"0018|1314": "Flip Angle",
"0018|0087": "Magnetic Field Strength",
"0018|1050": "Slice Thickness",
"0018|0088": "Spacing Between Slices",
"0020|1002": "Images in Acquisition",
}
for key in keys_to_extract:
output_dict[keys_to_extract[key]] = series_reader.GetMetaData(0, key)
except (RuntimeError, IndexError) as e:
# print(
# f"WARNING: Could not read DICOM tags from {input_dicom_dir}.",
# )
pass
return output_dict
def save_screenshot(
input_images: dict, output_filename: str = None, input_mask: str = None
) -> None:
"""
This function saves the screenshot of the input images and mask.
Args:
input_images (dict): The input multi-modal images.
output_filename (str, optional): The output filename to save the screenshot. Defaults to None.
input_mask (str, optional): The input mask filename. Defaults to None.
"""
# save the screenshot
images = (",").join(
[
input_images["T1"],
input_images["T1GD"],
input_images["T2"],
input_images["FLAIR"],
]
)
ylabels = (",").join(MODALITIES_LIST)
figure_generator(
input_images=images,
ylabels=ylabels,
output=output_filename,
input_mask=input_mask,
flip_sagittal=True,
flip_coronal=True,
)
def _read_image_with_min_check(filename):
"""
This function fixes negatives by scaling the image according to the following logic:
if min(input) < 0:
for all x in image:
if x != 0:
x -= min
Args:
filename (str): The input filename.
Returns:
sitk.Image: The read image.
int: The negative count.
"""
input_image = sitk.ReadImage(filename)
input_image_array = sitk.GetArrayFromImage(input_image)
min = np.min(input_image_array)
# the threshold above which an error is displayed, otherwise, the intensities are scaled
max_negative_count_threshold = 5000
if min < 0:
blobs = input_image_array < 0
all_labels_nonZero = np.nonzero(label(blobs))
_, counts = np.unique(all_labels_nonZero, return_counts=True)
if np.max(counts) < max_negative_count_threshold:
output_array = deepcopy(input_image_array)
mask = output_array != 0
output_array[mask] = output_array[mask] - min
output_image = sitk.GetImageFromArray(output_array)
output_image.CopyInformation(input_image)
sitk.WriteImage(output_image, filename)
return 0
else:
return counts.astype(int)
return 0
def _parse_csv_header(filename):
"""
Read filename and return the parsed headers.
Args:
filename (str): The input filename.
Returns:
dict: The parsed headers.
"""
with open(filename, "r") as csvfile:
datareader = csv.reader(csvfile)
headers = {} # save headers
for row in datareader:
for col in row:
temp = col.lower() # convert to lower case
temp = temp.replace(" ", "") # remove spaces
temp = temp.replace("_", "") # remove underscores
temp = temp.replace("-", "") # remove dashes
if temp in SUBJECT_NAMES:
headers["ID"] = col
elif temp in TIMEPOINT_NAMES:
headers["Timepoint"] = col
else:
for key in MODALITY_ID_DICT.keys():
if temp in MODALITY_ID_DICT[key]:
headers[key] = col
break
if "Timepoint" not in headers:
headers["Timepoint"] = None
return headers
def _copy_files_to_correct_location(interimOutputDir, finalSubjectOutputDir, subjectID):
"""
This function copies the intermediate files and final outputs to correct location and if these are absent, returns a bool flag stating that brats pipeline needs to run again
Args:
interimOutputDir (str): The interim output directory.
finalSubjectOutputDir (str): The final subject output directory.
subjectID (str): The subject ID.
Returns:
bool, dict: The flag stating whether brats pipeline needs to run again and the output files in the expected location.
"""
# copy files to correct location for inference and training
runBratsPipeline = False
input_files = {
k: posixpath.join(interimOutputDir, v) for k, v in INPUT_FILENAMES.items()
}
expected_outputs = get_expected_outputs(subjectID, finalSubjectOutputDir)
for key in input_files.keys():
if not os.path.exists(expected_outputs[key]):
if os.path.exists(input_files[key]):
shutil.copyfile(input_files[key], expected_outputs[key])
else:
runBratsPipeline = True
return runBratsPipeline, expected_outputs
def get_expected_outputs(subjectID: str, output_dir: str) -> dict:
expected_outputs = {
"ID": subjectID,
"T1": posixpath.join(output_dir, subjectID + "_t1.nii.gz"),
"T1GD": posixpath.join(output_dir, subjectID + "_t1c.nii.gz"),
"T2": posixpath.join(output_dir, subjectID + "_t2w.nii.gz"),
"FLAIR": posixpath.join(output_dir, subjectID + "_t2f.nii.gz"),
}
return expected_outputs
def get_brain_mask_files(subject_id, output_dir) -> dict:
files = {}
for modality in MODALITIES_LIST:
files[modality] = posixpath.join(
output_dir,
f"{subject_id}_brain_{MODALITY_ID_MAPPING[modality]}.nii.gz",
)
return files
def _run_brain_extraction_using_gandlf(
subject_id: str,
input_oriented_images: dict,
models_to_infer: Union[str, list],
base_output_dir: str,
) -> sitk.Image:
"""
This function runs brain extraction using gandlf.
Args:
subject_id (str): The subject ID.
input_oriented_images (dict): The input oriented images.
models_to_infer (Union[str, list]): The models to infer as list or as comma-separated string.
base_output_dir (str): The base output directory.
Returns:
sitk.Image: The fused brain mask.
"""
df_for_gandlf = pd.DataFrame(columns=GANDLF_DF_COLUMNS)
for key in MODALITIES_LIST:
current_modality = {
"SubjectID": subject_id + "_" + key,
"Channel_0": input_oriented_images[key],
}
df_for_gandlf = pd.concat(
[df_for_gandlf, pd.DataFrame(current_modality, index=[0])]
)
data_path = posixpath.join(base_output_dir, BRAIN_FILENAME)
df_for_gandlf.to_csv(
data_path,
index=False,
)
models_to_run = (
models_to_infer
if isinstance(models_to_infer, list)
else models_to_infer.split(",")
)
images_for_fusion = []
for model_dir in models_to_run:
model_id = os.path.basename(model_dir)
model_output_dir = posixpath.join(
base_output_dir, "brain_extraction_" + str(model_id)
)
file_list = os.listdir(model_dir)
for file in file_list:
if file.endswith(".yaml") or file.endswith(".yml"):
config_file = posixpath.join(model_dir, file)
break
main_run(
data_csv=data_path,
config_file=config_file,
model_dir=model_dir,
train_mode=False,
device="cpu",
resume=False,
reset=False,
output_dir=model_output_dir,
)
model_output_dir_testing = posixpath.join(model_output_dir, TESTING_FOLDER)
modality_outputs = os.listdir(model_output_dir_testing)
for modality in modality_outputs:
modality_output_dir = posixpath.join(model_output_dir_testing, modality)
files_in_modality = os.listdir(modality_output_dir)
for file in files_in_modality: # this loop may not be necessary
if file.endswith(".nii.gz"):
file_path = posixpath.join(modality_output_dir, file)
shutil.copyfile(
file_path,
posixpath.join(
base_output_dir,
f"brainMask_{model_id}_{modality}.nii.gz",
),
)
images_for_fusion.append(sitk.ReadImage(file_path, sitk.sitkUInt8))
return fuse_images(images_for_fusion, "staple", [0, 1])
def _run_tumor_segmentation_using_gandlf(
subject_id: str,
input_oriented_brain_images: dict,
models_to_infer: Union[str, list],
base_output_dir: str,
) -> sitk.Image:
"""
This function runs tumor segmentation using gandlf.
Args:
subject_id (str): The subject ID.
input_oriented_brain_images (dict): The input oriented brain images.
models_to_infer (Union[str, list]): The models to infer as list or as comma-separated string.
base_output_dir (str): The base output directory.
Returns:
sitk.Image: The fused tumor mask.
"""
df_for_gandlf = pd.DataFrame(columns=GANDLF_DF_COLUMNS)
current_subject = {"SubjectID": subject_id}
channel_idx = 0
# modality order (trained according to EC): t1,t2,flair,t1c
modality_order = ["T1", "T2", "FLAIR", "T1GD"]
# todo: confirm the order for modalities
for key in modality_order:
current_subject[f"Channel_{channel_idx}"] = input_oriented_brain_images[key]
channel_idx += 1
df_for_gandlf = pd.DataFrame(current_subject, index=[0])
data_path = posixpath.join(base_output_dir, TUMOR_FILENAME)
df_for_gandlf.to_csv(
data_path,
index=False,
)
models_to_run = (
models_to_infer
if isinstance(models_to_infer, list)
else models_to_infer.split(",")
)
tumor_masks_to_return = []
images_for_fusion = []
mask_output_dir = posixpath.join(base_output_dir, TUMOR_MASK_FOLDER)
os.makedirs(mask_output_dir, exist_ok=True)
for model_dir in models_to_run:
model_id = os.path.basename(model_dir)
model_output_dir = posixpath.join(
base_output_dir, "tumor_segmentation_" + str(model_id)
)
file_list = os.listdir(model_dir)
for file in file_list:
if file.endswith(".yaml") or file.endswith(".yml"):
config_file = posixpath.join(model_dir, file)
break
# ensure the openvino version is used
# NOTE: if we need this, make sure there are proper permissions
# when rewriting the config file
# parameters = yaml.safe_load(open(config_file, "r"))
# parameters["model"]["type"] = "openvino"
# yaml.safe_dump(parameters, open(config_file, "w"))
main_run(
data_csv=data_path,
config_file=config_file,
model_dir=model_dir,
train_mode=False,
device="cpu",
resume=False,
reset=False,
output_dir=model_output_dir,
)
model_output_dir_testing = posixpath.join(model_output_dir, TESTING_FOLDER)
# We expect one subject (one output modality, one file).
subject = os.listdir(model_output_dir_testing)[0]
subject_output_dir = posixpath.join(model_output_dir_testing, subject)
files_in_modality = os.listdir(subject_output_dir)
for file in files_in_modality: # this loop may not be necessary
if file.endswith(".nii.gz"):
file_path = posixpath.join(subject_output_dir, file)
renamed_path = posixpath.join(
mask_output_dir,
f"{subject_id}_tumorMask_model-{model_id}.nii.gz",
)
shutil.copyfile(file_path, renamed_path)
# Append the renamed path to keep track of model IDs
tumor_masks_to_return.append(renamed_path)
images_for_fusion.append(sitk.ReadImage(file_path, sitk.sitkUInt8))
fused_masks_to_return = generate_tumor_segmentation_fused_images(images_for_fusion, mask_output_dir, subject_id)
return tumor_masks_to_return + fused_masks_to_return
def generate_tumor_segmentation_fused_images(images_for_fusion, mask_output_dir, subject_id):
tumor_class_list = [0, 1, 2, 3, 4]
fused_masks_to_return = []
if len(images_for_fusion) > 1:
for fusion_type in ["staple", "simple", "voting"]:
fused_mask = fuse_images(images_for_fusion, fusion_type, tumor_class_list)
fused_mask_file = posixpath.join(
mask_output_dir,
f"{subject_id}_tumorMask_fused-{fusion_type}.nii.gz",
)
sitk.WriteImage(fused_mask, fused_mask_file)
fused_masks_to_return.append(fused_mask_file)
return fused_masks_to_return
class Preparator:
def __init__(self, input_csv: str, output_dir: str, executablePath: str):
self.input_csv = input_csv
self.input_dir = str(Path(input_csv).parent)
self.output_dir = os.path.normpath(output_dir)
self.interim_output_dir = posixpath.join(self.output_dir, INTERIM_FOLDER)
self.final_output_dir = posixpath.join(self.output_dir, FINAL_FOLDER)
self.subjects_file = posixpath.join(self.final_output_dir, SUBJECTS_FILENAME)
self.neg_subjects_file = posixpath.join(
self.final_output_dir, NEG_SUBJECTS_FILENAME
)
self.failing_subjects_file = posixpath.join(
self.final_output_dir, FAIL_SUBJECTS_FILENAME
)
self.dicom_tag_information_to_write_anon_file = posixpath.join(
self.final_output_dir, DICOM_ANON_FILENAME
)
self.dicom_tag_information_to_write_collab_file = posixpath.join(
self.final_output_dir, DICOM_COLLAB_FILENAME
)
self.__init_out_dfs()
self.stdout_log = posixpath.join(self.output_dir, STDOUT_FILENAME)
self.stderr_log = posixpath.join(self.output_dir, STDERR_FILENAME)
self.dicom_tag_information_to_write_collab = {}
self.dicom_tag_information_to_write_anon = {}
self.brats_pipeline_exe = executablePath
if self.brats_pipeline_exe is None:
self.brats_pipeline_exe = posixpath.join(
Path(__file__).parent.resolve(), EXEC_NAME
)
if platform.system() == "Windows":
if not self.brats_pipeline_exe.endswith(".exe"):
self.brats_pipeline_exe += ".exe"
def __init_out_dfs(self):
self.subjects = pd.DataFrame(
columns=["SubjectID", "Timepoint", "T1", "T1GD", "T2", "FLAIR"]
)
self.neg_subjects = pd.DataFrame(
columns=["SubjectID", "Timepoint", "Modality", "Count"]
)
self.failing_subjects = pd.DataFrame(columns=["SubjectID", "Timepoint"])
def validate(self):
assert os.path.exists(self.input_csv), "Input CSV file not found"
assert (
shutil.which(self.brats_pipeline_exe) is not None
), "BraTS Pipeline executable not found, please contact admin@fets.ai for help."
def process_data(self):
items = self.subjects_df.iterrows()
total = self.subjects_df.shape[0]
pbar = tqdm(range(total), desc="Preparing Dataset (1-10 min per subject)")
for idx, (_, row) in enumerate(items):
self.process_row(idx, row, pbar)
def process_row(self, idx: int, row: pd.Series, pbar: tqdm):
self.convert_to_dicom(idx, row, pbar)
self.extract_brain(row, pbar)
self.extract_tumor(row, pbar)
def __get_row_information(self, row: pd.Series):
parsed_headers = self.parsed_headers
subject_id = row[self.parsed_headers["ID"]]
subject_id_timepoint = subject_id
# create QC and Final output dirs for each subject
interimOutputDir_actual = posixpath.join(
self.interim_output_dir, subject_id_timepoint
)
finalSubjectOutputDir_actual = posixpath.join(
self.final_output_dir, subject_id_timepoint
)
# per the data ingestion step, we are creating a new folder called timepoint, can join timepoint to subjectid if needed
if parsed_headers["Timepoint"] is not None:
timepoint = row[parsed_headers["Timepoint"]]
subject_id_timepoint += "_" + timepoint
interimOutputDir_actual = posixpath.join(interimOutputDir_actual, timepoint)
finalSubjectOutputDir_actual = posixpath.join(
finalSubjectOutputDir_actual, timepoint
)
return (
subject_id,
timepoint,
subject_id_timepoint,
interimOutputDir_actual,
finalSubjectOutputDir_actual,
)
def convert_to_dicom(self, idx: int, row: pd.Series, pbar: tqdm):
parsed_headers = self.parsed_headers
bratsPipeline_exe = self.brats_pipeline_exe
(
subject_id,
timepoint,
subject_id_timepoint,
interimOutputDir_actual,
finalSubjectOutputDir_actual,
) = self.__get_row_information(row)
# create QC and Final output dirs for each subject
Path(interimOutputDir_actual).mkdir(parents=True, exist_ok=True)
Path(finalSubjectOutputDir_actual).mkdir(parents=True, exist_ok=True)
pbar.set_description(f"Processing {subject_id_timepoint}")
# get the relevant dicom tags
self.dicom_tag_information_to_write_collab[subject_id_timepoint] = {}
self.dicom_tag_information_to_write_anon[str(idx)] = {}
for modality in MODALITIES_LIST:
tags_from_modality = _get_relevant_dicom_tags(row[parsed_headers[modality]])
self.dicom_tag_information_to_write_collab[subject_id_timepoint][
modality
] = tags_from_modality
with open(
posixpath.join(
interimOutputDir_actual, f"dicom_tag_information_{modality}.yaml"
),
"w",
) as f:
yaml.safe_dump(tags_from_modality, f, allow_unicode=True)
self.dicom_tag_information_to_write_anon[str(idx)][
modality
] = tags_from_modality
interimOutputDir_actual_reoriented = posixpath.join(
interimOutputDir_actual, REORIENTED_FOLDER
)
Path(interimOutputDir_actual_reoriented).mkdir(parents=True, exist_ok=True)
# if files already exist in DataForQC, then copy to "reorient" folder, and if files exist in "reorient" folder, then skip
runBratsPipeline, _ = _copy_files_to_correct_location(
interimOutputDir_actual,
interimOutputDir_actual_reoriented,
subject_id_timepoint,
)
# check if the files exist already, if so, skip
if runBratsPipeline:
pbar.set_description(f"Running BraTSPipeline")
command = (
bratsPipeline_exe
+ " -t1 "
+ row[parsed_headers["T1"]]
+ " -t1c "
+ row[parsed_headers["T1GD"]]
+ " -t2 "
+ row[parsed_headers["T2"]]
+ " -fl "
+ row[parsed_headers["FLAIR"]]
+ " -s 0 -o "
+ interimOutputDir_actual
)
with open(self.stdout_log, "a+") as out, open(self.stderr_log, "a+") as err:
out.write(f"***\n{command}\n***")
err.write(f"***\n{command}\n***")
subprocess.Popen(command, stdout=out, stderr=err, shell=True).wait()
runBratsPipeline, outputs_reoriented = _copy_files_to_correct_location(
interimOutputDir_actual,
interimOutputDir_actual_reoriented,
subject_id_timepoint,
)
if runBratsPipeline:
# The BraTS command failed, and no files were found
# flag this subject as failing
failing_data = {"SubjectID": subject_id, "Timepoint": timepoint}
failing_subject = pd.DataFrame(failing_data, index=[0])
self.failing_subjects = pd.concat([self.failing_subjects, failing_subject])
return
# store the outputs in a dictionary when there are no errors
negatives_detected = False
for modality in MODALITIES_LIST:
count = _read_image_with_min_check(outputs_reoriented[modality])
# if there are any negative values, then store the subjectid, timepoint, modality and count of negative values
if count == 0:
continue
neg_data = {
"SubjectID": subject_id,
"Timepoint": timepoint,
"Modality": modality,
"Count": count,
}
neg_subject = pd.DataFrame(neg_data, index=[0])
self.neg_subjects = pd.concat([self.neg_subjects, neg_subject])
negatives_detected = True
# store the outputs in a dictionary when there are no errors
if negatives_detected:
return
subject_data = {
"SubjectID": subject_id,
"Timepoint": timepoint,
"T1": outputs_reoriented["T1"],
"T1GD": outputs_reoriented["T1GD"],
"T2": outputs_reoriented["T2"],
"FLAIR": outputs_reoriented["FLAIR"],
}
subject = pd.DataFrame(subject_data, index=[0])
self.subjects = pd.concat(
[
self.subjects,
subject,
]
)
pbar.set_description(f"Saving screenshot")
screenshot_path = posixpath.join(
interimOutputDir_actual_reoriented,
f"{subject_id_timepoint}_summary_coregistration.png",
)
# save the screenshot
save_screenshot(outputs_reoriented, screenshot_path)
if os.path.exists(screenshot_path):
shutil.copyfile(
screenshot_path,
posixpath.join(
interimOutputDir_actual,
f"{subject_id_timepoint}_summary_coregistration.png",
),
)
def extract_brain(self, row: pd.Series, pbar: tqdm):
(
*_,
subject_id_timepoint,
interimOutputDir_actual,
finalSubjectOutputDir_actual,
) = self.__get_row_information(row)
interimOutputDir_actual_reoriented = posixpath.join(
interimOutputDir_actual, REORIENTED_FOLDER
)
outputs_reoriented = get_expected_outputs(
subject_id_timepoint, interimOutputDir_actual_reoriented
)
# Check for existence of brain mask.
# That way, we can pass corrected brain masks and proceed without
# overwriting the mask.
brain_mask_path = posixpath.join(
interimOutputDir_actual, "brainMask_fused.nii.gz"
)
if not os.path.exists(brain_mask_path):
pbar.set_description(f"Brain Extraction")
models_dir = posixpath.join(Path(__file__).parent.resolve(), "data_prep_models")
brain_extraction_models_dir = posixpath.join(models_dir, "brain_extraction")
brain_extraction_models = [
posixpath.join(brain_extraction_models_dir, model_dir)
for model_dir in os.listdir(brain_extraction_models_dir)
]
brain_mask = _run_brain_extraction_using_gandlf(
subject_id_timepoint,
outputs_reoriented,
brain_extraction_models,
interimOutputDir_actual,
)
sitk.WriteImage(brain_mask, brain_mask_path)
else:
brain_mask = sitk.ReadImage(brain_mask_path)
# this is to ensure that the mask and reoriented images are in the same byte order
# brain_mask = sitk.Cast(brain_mask, sitk.sitkFloat32)
input_for_tumor_models = get_brain_mask_files(
subject_id_timepoint, finalSubjectOutputDir_actual
)
for modality in MODALITIES_LIST:
image = sitk.ReadImage(outputs_reoriented[modality])
masked_image = sitk.Mask(image, brain_mask)
file_to_save = input_for_tumor_models[modality]
sitk.WriteImage(masked_image, file_to_save)
# save the screenshot
save_screenshot(
input_for_tumor_models,
posixpath.join(
interimOutputDir_actual,
f"{subject_id_timepoint}_summary_brain-extraction.png",
),
brain_mask_path,
)
def extract_tumor(self, row: pd.Series, pbar: tqdm):
(
*_,
subject_id_timepoint,
interimOutputDir_actual,
finalSubjectOutputDir_actual,
) = self.__get_row_information(row)
input_for_tumor_models = get_brain_mask_files(
subject_id_timepoint, finalSubjectOutputDir_actual
)
pbar.set_description(f"Brain Tumor Segmentation")
models_dir = posixpath.join(Path(__file__).parent.resolve(), "data_prep_models")
tumor_segmentation_models_dir = posixpath.join(models_dir, "tumor_segmentation")
tumor_segmentation_models = [
posixpath.join(tumor_segmentation_models_dir, model_dir)
for model_dir in os.listdir(tumor_segmentation_models_dir)
]
tumor_masks_for_qc = _run_tumor_segmentation_using_gandlf(
subject_id_timepoint,
input_for_tumor_models,
tumor_segmentation_models,
interimOutputDir_actual,
)
for tumor_mask in tumor_masks_for_qc:
tumor_mask_id = os.path.basename(tumor_mask).replace(".nii.gz", "")
# save the screenshot
save_screenshot(
input_for_tumor_models,
posixpath.join(interimOutputDir_actual, f"{tumor_mask_id}_summary.png"),
tumor_mask,
)
with open(self.stdout_log, "a+") as f:
f.write(f"***\nTumor Masks For QC:\n{tumor_masks_for_qc}\n***")
def write(self):
if self.subjects.shape[0]:
self.subjects.to_csv(self.subjects_file, index=False)
if self.neg_subjects.shape[0]:
self.neg_subjects.to_csv(self.neg_subjects_file, index=False)
if self.failing_subjects.shape[0]:
self.failing_subjects.to_csv(self.failing_subjects_file, index=False)
with open(self.dicom_tag_information_to_write_collab_file, "w") as f:
yaml.safe_dump(
self.dicom_tag_information_to_write_collab, f, allow_unicode=True
)
with open(self.dicom_tag_information_to_write_anon_file, "w") as f:
yaml.safe_dump(
self.dicom_tag_information_to_write_anon, f, allow_unicode=True
)
def read(self):
self.parsed_headers = _parse_csv_header(self.input_csv)
self.subjects_df = pd.read_csv(self.input_csv, dtype=str)
if os.path.exists(self.subjects_file):
self.subjects = pd.read_csv(self.subjects_file)
if os.path.exists(self.neg_subjects_file):
self.neg_subjects = pd.read_csv(self.neg_subjects_file)
if os.path.exists(self.failing_subjects_file):
self.failing_subjects = pd.read_csv(self.failing_subjects_file)
if os.path.exists(self.dicom_tag_information_to_write_collab_file):
with open(self.dicom_tag_information_to_write_collab_file, "r") as f:
self.dicom_tag_information_to_write_collab = yaml.safe_load(f)
if os.path.exists(self.dicom_tag_information_to_write_anon_file):
with open(self.dicom_tag_information_to_write_anon_file, "r") as f:
self.dicom_tag_information_to_write_anon = yaml.safe_load(f)
def main():
parser = setup_parser()
args = parser.parse_args()
prep = Preparator(args.inputCSV, args.outputDir, args.executablePath)
prep.validate()
prep.read()
prep.process_data()
prep.write()
if __name__ == "__main__":
if platform.system().lower() == "darwin":
sys.exit("macOS is not supported")
else:
main()