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preliminary MRI functionality (MR-RTSTRUCT pairs)
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Former-commit-id: 3c01806
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@skim2257
skim2257 committed Jan 18, 2022
1 parent bc9c6a2 commit b4abcac
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Showing 6 changed files with 58 additions and 173 deletions.
12 changes: 6 additions & 6 deletions imgtools/autopipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,19 +94,19 @@ def process_one_subject(self, subject_id):
for i, colname in enumerate(self.output_streams):
modality = colname.split("_")[0]

#Taking modality pairs if it exists till _{num}
# Taking modality pairs if it exists till _{num}
output_stream = ("_").join([item for item in colname.split("_") if item.isnumeric()==False])

#If there are multiple connections existing, multiple connections means two modalities connected to one modality. They end with _1
# If there are multiple connections existing, multiple connections means two modalities connected to one modality. They end with _1
mult_conn = colname.split("_")[-1].isnumeric()
num = colname.split("_")[-1]

print(output_stream)

if read_results[i] is None:
print("The subject id: {} has no {}".format(subject_id,colname))
print("The subject id: {} has no {}".format(subject_id, colname))
pass
elif modality == "CT":
elif modality == "CT" or modality == 'MR':
image = read_results[i]
if len(image.GetSize()) == 4:
assert image.GetSize()[-1] == 1, f"There is more than one volume in this CT file for {subject_id}."
Expand Down Expand Up @@ -142,7 +142,7 @@ def process_one_subject(self, subject_id):
conn_to = output_stream.split("_")[-1]

# make_binary_mask relative to ct/pet
if conn_to == "CT":
if conn_to == "CT" or conn_to == "MR":
mask = self.make_binary_mask(structure_set, image)
elif conn_to == "PT":
mask = self.make_binary_mask(structure_set, pet)
Expand Down Expand Up @@ -178,7 +178,7 @@ def process_one_subject(self, subject_id):
return

def save_data(self):
files = glob.glob(os.path.join(self.output_directory,".temp","*.pkl"))
files = glob.glob(os.path.join(self.output_directory, ".temp", "*.pkl"))
for file in files:
filename = pathlib.Path(file).name
subject_id = os.path.splitext(filename)[0]
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16 changes: 9 additions & 7 deletions imgtools/io/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,11 +41,13 @@ def file_name_convention() -> Dict:
This function returns the file name taxonomy which is used by ImageAutoOutput and Dataset class
"""
file_name_convention = {"CT": "image",
"RTDOSE_CT": "dose",
"RTSTRUCT_CT": "mask_ct.seg",
"RTSTRUCT_PT": "mask_pt.seg",
"PT_CT": "pet",
"PT": "pet",
"RTDOSE": "dose",
"RTSTRUCT": "mask.seg"}
"MR": "image",
"RTDOSE_CT": "dose",
"RTSTRUCT_CT": "mask_ct.seg",
"RTSTRUCT_MR": "mask_mr.seg",
"RTSTRUCT_PT": "mask_pt.seg",
"PT_CT": "pet",
"PT": "pet",
"RTDOSE": "dose",
"RTSTRUCT": "mask.seg"}
return file_name_convention
130 changes: 3 additions & 127 deletions imgtools/io/loaders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
from ..modules import StructureSet
from ..modules import Dose
from ..modules import PET
from ..utils.crawl import *



Expand Down Expand Up @@ -76,13 +77,13 @@ def read_dicom_rtdose(path):
def read_dicom_pet(path,series=None):
return PET.from_dicom_pet(path,series, "SUV")

def read_dicom_auto(path,series=None):
def read_dicom_auto(path, series=None):
if path is None:
return None
dcms = glob.glob(os.path.join(path, "*.dcm"))
meta = dcmread(dcms[0])
modality = meta.Modality
if modality == 'CT':
if modality == 'CT' or modality == 'MR':
return read_dicom_series(path,series)
elif modality == 'PT':
return read_dicom_pet(path,series)
Expand Down Expand Up @@ -176,131 +177,6 @@ def keys(self):
def items(self):
return ((k, self[k]) for k in self.keys())

class ImageAutoLoader(BaseLoader):
def __init__(self,
path: str,
modality: List[str] = ['CT']):
self.top = path
self.modality = modality
self.database_df = self.crawl(path)

# sort by modality
self.rtstruct = self.database_df[self.database_df['modality'] == 'RTSTRUCT']
self.rtdose = self.database_df[self.database_df['modality'] == 'RTDOSE']
self.pet = self.database_df[self.database_df['modality'] == 'PT']

@staticmethod
def crawl_one(folder):
database = {}
for path, directories, files in os.walk(folder):
# find dicoms
dicoms = glob.glob(os.path.join(path, "*.dcm"))

# instance (slice) information
for dcm in dicoms:
meta = dcmread(dcm)
patient = meta.PatientID
study = meta.StudyInstanceUID
series = meta.SeriesInstanceUID
instance = meta.SOPInstanceUID

try:
reference = meta.FrameOfReferenceUID
except:
try:
reference = meta.ReferencedFrameOfReferenceSequence[0].FrameOfReferenceUID
except:
reference = ""

try:
study_description = meta.StudyDescription
except:
study_description = ""

try:
series_description = meta.SeriesDescription
except:
series_description = ""

if patient not in database:
database[patient] = {}
if study not in database[patient]:
database[patient][study] = {'description': study_description}
if series not in database[patient][study]:
database[patient][study][series] = {'instances': 0,
'modality': meta.Modality,
'description': series_description,
'reference': reference,
'folder': path}
database[patient][study][series]['instances'] += 1

return database

@staticmethod
def to_df(database_dict):
df = pd.DataFrame(columns=['patient_ID', 'study', 'study_description', 'series', 'series_description', 'modality', 'instances', 'folder'])
for pat in database_dict:
for study in database_dict[pat]:
for series in database_dict[pat][study]:
if series != 'description':
df = df.append({'patient_ID': pat,
'study': study,
'study_description': database_dict[pat][study]['description'],
'series': series,
'series_description': database_dict[pat][study][series]['description'],
'modality': database_dict[pat][study][series]['modality'],
'instances': database_dict[pat][study][series]['instances'],
'reference': database_dict[pat][study][series]['reference'],
'folder': database_dict[pat][study][series]['folder']}, ignore_index=True)
return df

def crawl(self, top, n_jobs=1):
database_list = []
folders = glob.glob(os.path.join(top, "*"))

# crawl folder-by-folder
if n_jobs == 1:
for n, folder in enumerate(tqdm(folders)):
database_list.append(self.crawl_one(folder))
else:
database_list = Parallel(n_jobs=n_jobs)(delayed(self.crawl_one)(os.path.join(top, folder)) for folder in tqdm(folders))

# convert list to dictionary
database_dict = {}
for db in database_list:
for key in db:
database_dict[key] = db[key]

# save one level above imaging folders
parent, dataset = os.path.split(top)

# save as json
with open(os.path.join(parent, f'imgtools_{dataset}.json'), 'w') as f:
json.dump(database_dict, f, indent=4)

# save as dataframe
df = self.to_df(database_dict)
df_path = os.path.join(parent, f'imgtools_{dataset}.csv')
df.to_csv(df_path)

print(f"imgtools dataset {dataset} crawling complete. You can find the spreadsheet at: {df_path}")

return self.filter(df)


def __getitem__(self, subject_id):
row = self.df['patient_ID' == subject_id]
paths = {col: row[col] for col in self.colnames}
if self.expand_paths:
paths = {col: glob.glob(path)[0] for col, path in paths.items()}
outputs = {col: self.readers[i](path) for i, (col, path) in enumerate(paths.items())}
return self.output_tuple(**outputs)

def keys(self):
return list(self.paths.index)

def items(self):
return ((k, self[k]) for k in self.keys())

class ImageFileLoader(BaseLoader):
def __init__(self,
Expand Down
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