def run(self):
images_T1 = natural_sort(extract_file_paths(os.path.join(self._root_dir, "T1")))
labels = natural_sort(extract_file_paths(os.path.join(self._root_dir, "label")))
files = np.stack((np.array(images_T1), np.array(labels)), axis=1)
# self._dispatch_jobs(files, 8)
self._do_job(files)
def run(self, output_filename: str):
source_paths_t2 = list()
source_paths_t1 = list()
target_paths = list()
for subject in sorted(os.listdir(os.path.join(self._source_dir))):
source_paths_t1.append(
extract_file_paths(
os.path.join(self._source_dir, subject, "T1")))
source_paths_t2.append(
extract_file_paths(
os.path.join(self._source_dir, subject, "T2")))
target_paths.append(
extract_file_paths(
os.path.join(self._source_dir, subject, "Labels")))
subjects = np.arange(1, 11)
source_paths_t1 = natural_sort(
[item for sublist in source_paths_t1 for item in sublist])
source_paths_t2 = natural_sort(
[item for sublist in source_paths_t2 for item in sublist])
target_paths = natural_sort(
[item for sublist in target_paths for item in sublist])
with open(os.path.join(self._output_dir, output_filename),
mode='a+') as output_file:
writer = csv.writer(output_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
writer.writerow([
"T1", "T2", "labels", "subject", "T1_min", "T1_max", "T1_mean",
"T1_std", "T2_min", "T2_max", "T2_mean", "T2_std"
])
for source_path, source_path_t2, target_path, subject in zip(
source_paths_t1, source_paths_t2, target_paths, subjects):
self.LOGGER.info("Processing file {}".format(source_path))
t1 = ToNumpyArray()(source_path)
t2 = ToNumpyArray()(source_path_t2)
csv_data = np.vstack((source_path, source_path_t2, target_path,
subject, str(t1.min()), str(t1.max()),
str(t1.mean()), str(t1.std()),
str(t2.min()), str(t2.max()),
str(t2.mean()), str(t2.std())))
for item in range(csv_data.shape[1]):
writer.writerow([
csv_data[0][item], csv_data[1][item],
csv_data[2][item], csv_data[3][item],
csv_data[4][item], csv_data[5][item],
csv_data[6][item], csv_data[7][item],
csv_data[8][item], csv_data[9][item],
csv_data[10][item], csv_data[11][item]
])
output_file.close()
def run(self, prefix: str = ""):
images_T1 = natural_sort(
extract_file_paths(os.path.join(self._root_dirs["iSEG"], "T1")))
labels = natural_sort(
extract_file_paths(os.path.join(self._root_dirs["iSEG"], "label")))
files = np.stack((np.array(images_T1), np.array(labels)), axis=1)
self._dataset_mean_iSEG = np.mean(
self._dispatch_jobs_in_pool(files, 5, self._get_mean_iseg))
self._dataset_std_iSEG = np.mean(
self._dispatch_jobs_in_pool(files, 5, self._get_std_iseg))
files = list()
for subject in sorted(
os.listdir(os.path.join(self._root_dirs["MRBrainS"]))):
files.append(
extract_file_paths(
os.path.join(self._root_dirs["MRBrainS"], subject)))
self._dataset_mean_MRBrainS = np.mean(
self._dispatch_jobs_in_pool(files, 5, self._get_mean_mrbrains))
self._dataset_std_MRBrainS = np.mean(
self._dispatch_jobs_in_pool(files, 5, self._get_std_mrbrains))
files = pandas.read_csv(self._root_dirs["ABIDE"])
images_T1 = np.asarray(files["T1"])
labels = np.asarray(files["labels"])
files = np.stack((np.array(images_T1), np.array(labels)), axis=1)
self._dataset_mean_ABIDE = np.mean(
self._dispatch_jobs_in_pool(files, 8, self._get_mean_abide))
self._dataset_std_ABIDE = np.mean(
self._dispatch_jobs_in_pool(files, 8, self._get_std_abide))
print("Triple Dataset mean: {}".format(
np.mean([
self._dataset_mean_iSEG, self._dataset_mean_MRBrainS,
self._dataset_mean_ABIDE
])))
print("Triple Dataset std: {}".format(
np.sqrt(
np.mean([
self._dataset_std_iSEG, self._dataset_std_MRBrainS,
self._dataset_std_ABIDE
]))))
print("Dual Dataset mean: {}".format(
np.mean([self._dataset_mean_iSEG, self._dataset_mean_MRBrainS])))
print("Dual Dataset std: {}".format(
np.sqrt(
np.mean([self._dataset_std_iSEG,
self._dataset_std_MRBrainS]))))
def run(self, output_filename: str):
source_paths = list()
target_paths = list()
subjects = list()
sites = list()
for dir in sorted(os.listdir(self._source_dir)):
source_paths_ = extract_file_paths(
os.path.join(self._source_dir, dir, "mri", "T1"), "T1.nii.gz")
target_paths_ = extract_file_paths(
os.path.join(self._source_dir, dir, "mri", "Labels"),
"Labels.nii.gz")
subject_ = dir
source_paths.append(source_paths_)
target_paths.append(target_paths_)
if len(source_paths_) is not 0:
match = re.search('(?P<site>.*)_(?P<patient_id>[0-9]*)',
str(dir))
site_ = match.group("site")
sites.append(site_)
subjects.append(subject_)
source_paths = list(filter(None, source_paths))
target_paths = list(filter(None, target_paths))
with open(os.path.join(self._output_dir, output_filename),
mode='a+') as output_file:
writer = csv.writer(output_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
writer.writerow([
"T1", "labels", "subject", "site", "min", "max", "mean", "std"
])
for source_path, target_path, subject, site in zip(
source_paths, target_paths, subjects, sites):
self.LOGGER.info("Processing file {}".format(source_path))
image = ToNumpyArray()(source_path[0])
csv_data = np.vstack(
(source_path, target_path, subject, site, (image.min()),
(image.max()), (image.mean()), (image.std())))
for item in range(csv_data.shape[1]):
writer.writerow([
csv_data[0][item], csv_data[1][item],
csv_data[2][item], csv_data[3][item],
csv_data[4][item], csv_data[5][item],
csv_data[6][item], csv_data[7][item]
])
output_file.close()
def run(self):
source_paths = list()
for subject in sorted(os.listdir(os.path.join(self._root_dir))):
source_paths.append(extract_file_paths(os.path.join(self._root_dir, subject)))
self._dispatch_jobs(source_paths, 8)
def setUp(self) -> None:
paths = extract_file_paths(self.PATH)
self._dataset = MRBrainSSegmentationFactory.create(
natural_sort(paths), None, modalities=Modality.T1, dataset_id=0)
self._reconstructor = ImageReconstructor([256, 256, 192],
[1, 32, 32, 32], [1, 8, 8, 8])
transforms = Compose(
[ToNumpyArray(),
PadToPatchShape([1, 32, 32, 32], [1, 8, 8, 8])])
self._full_image = transforms(self.FULL_IMAGE_PATH)
