def __init__(self, root_path: str, subject: str, patch_size, step):
self._image_path = os.path.join(root_path, subject, "T1", "T1.nii.gz")
self._image = PadToShape(target_shape=[1, 256, 256, 192])(ToNumpyArray()(self._image_path))
self._slices = SliceBuilder(self._image, patch_size=patch_size, step=step).build_slices(
keep_centered_on_foreground=True)
self._image_max = self._image.max()
self._patch_size = patch_size
self._step = step
def _crop_to_content(self, t1, labels):
self._transform_crop = transforms.Compose(
[CropToContent(),
PadToShape(self._normalized_shape)])
try:
sample = Sample(x=t1, y=labels, is_labeled=True)
transformed_image = self._transform_crop(sample)
return transformed_image.x, transformed_image.y
except Exception as e:
self.LOGGER.warning(e)
def __init__(self,
root_dir_iseg: str,
root_dir_mrbrains: str,
output_dir: str,
params: dict = None):
self._root_dir_iseg = root_dir_iseg
self._root_dir_mrbrains = root_dir_mrbrains
self._output_dir = output_dir
self._normalized_shape = self.compute_normalized_shape_from_images_in(
self._root_dir_iseg, self._root_dir_mrbrains)
self._transforms = transforms.Compose([
ToNumpyArray(),
CropToContent(),
PadToShape(self._normalized_shape)
])
self._params = params
def __init__(self,
images: List[str],
patch_size: Tuple[int, int, int, int],
reconstructed_image_size: Tuple[int, int, int, int],
step: Tuple[int, int, int, int],
batch_size: int = 5,
models: List[torch.nn.Module] = None,
normalize: bool = False,
is_ground_truth: bool = False,
normalize_and_segment: bool = False,
is_multimodal=False,
alpha=0.0,
prob_bias=0.0,
snr=0.0,
prob_noise=0.0):
self._patch_size = patch_size
self._reconstructed_image_size = reconstructed_image_size
self._step = step
self._models = models
self._do_normalize = normalize
self._is_ground_truth = is_ground_truth
self._do_normalize_and_segment = normalize_and_segment
self._is_multimodal = is_multimodal
self._batch_size = batch_size
self._alpha = alpha
self._snr = snr
self._prob_bias = prob_bias
self._prob_noise = prob_noise
transformed_images = []
for image in images:
transform = Compose([
ToNumpyArray(),
PadToShape(target_shape=self._reconstructed_image_size)
])
transformed_images.append(transform(image))
self._images = transformed_images
self._overlap_maps = list(
map(
lambda image: SliceBuilder(image, self._patch_size, self._step)
.build_overlap_map(), self._images))
class SingleImageDataset(Dataset):
def __init__(self, root_path: str, subject: str, patch_size, step):
self._image_path = os.path.join(root_path, subject, "T1", "T1.nii.gz")
self._image = PadToShape(target_shape=[1, 256, 256, 192])(ToNumpyArray()(self._image_path))
self._slices = SliceBuilder(self._image, patch_size=patch_size, step=step).build_slices(
keep_centered_on_foreground=True)
self._image_max = self._image.max()
self._patch_size = patch_size
self._step = step
def __getitem__(self, index):
try:
image = torch.tensor([(self._image[self._slices[index]])], dtype=torch.float32,
requires_grad=False).squeeze(0)
return image
except Exception as e:
pass
def __len__(self):
return len(self._slices)
@property
def image_shape(self):
return self._image.shape
def run(self, prefix="standardize_"):
images_np = list()
headers = list()
file_names = list()
root_dirs = list()
root_dirs_number = list()
EXCLUDED = ["ROI", "label", "Normalized"]
for root, dirs, files in os.walk(os.path.join(
self._root_dir_mrbrains)):
root_dir_number = os.path.basename(os.path.normpath(root))
images = list(filter(re.compile(r"^T.*\.nii").search, files))
for file in images:
try:
self.LOGGER.info("Processing: {}".format(file))
file_names.append(file)
root_dirs.append(root)
root_dirs_number.append(root_dir_number)
images_np.append(self._transforms(os.path.join(root,
file)))
headers.append(
self._get_image_header(os.path.join(root, file)))
except Exception as e:
self.LOGGER.warning(e)
for root, dirs, files in os.walk(os.path.join(self._root_dir_iseg)):
if os.path.basename(os.path.normpath(root)) in EXCLUDED:
continue
root_dir_number = os.path.basename(os.path.normpath(root))
images = list(filter(re.compile(r".*T.*\.nii").search, files))
for file in images:
try:
self.LOGGER.info("Processing: {}".format(file))
file_names.append(file)
root_dirs.append(root)
root_dirs_number.append(root_dir_number)
images_np.append(self._transforms(os.path.join(root,
file)))
headers.append(
self._get_image_header(os.path.join(root, file)))
except Exception as e:
self.LOGGER.warning(e)
images = np.array(images_np).astype(np.float32)
transformed_images = np.subtract(images,
np.mean(images)) / np.std(images)
for i in range(transformed_images.shape[0]):
if "MRBrainS" in root_dirs[i]:
root_dir_number = os.path.basename(
os.path.normpath(root_dirs[i]))
if not os.path.exists(
os.path.join(
self._output_dir, "MRBrainS/Dual_Standardized/{}".
format(root_dir_number))):
os.makedirs(
os.path.join(
self._output_dir,
"MRBrainS/Dual_Standardized/{}".format(
root_dir_number)))
transforms_ = transforms.Compose([
ToNifti1Image(),
NiftiToDisk(
os.path.join(
os.path.join(
self._output_dir,
os.path.join("MRBrainS/Dual_Standardized",
root_dir_number)),
prefix + file_names[i]))
])
transforms_(transformed_images[i])
elif "iSEG" in root_dirs[i]:
root_dir_number = os.path.basename(
os.path.normpath(root_dirs[i]))
if not os.path.exists(
os.path.join(
self._output_dir, "iSEG/Dual_Standardized/{}".
format(root_dir_number))):
os.makedirs(
os.path.join(
self._output_dir,
"iSEG/Dual_Standardized/{}".format(
root_dir_number)))
transforms_ = transforms.Compose([
ToNifti1Image(),
NiftiToDisk(
os.path.join(
os.path.join(
self._output_dir,
os.path.join("iSEG/Dual_Standardized",
root_dir_number)),
prefix + file_names[i]))
])
transforms_(transformed_images[i])
for root, dirs, files in os.walk(self._root_dir_mrbrains):
root_dir_end = os.path.basename(os.path.normpath(root))
images = list(
filter(re.compile(r"^LabelsFor.*\.nii").search, files))
for file in images:
if not os.path.exists(
os.path.join(
self._output_dir,
os.path.join("MRBrainS/Dual_Standardized",
root_dir_end))):
os.makedirs(
os.path.join(
self._output_dir,
os.path.join("MRBrainS/Dual_Standardized",
root_dir_end)))
transforms_ = transforms.Compose([
ToNumpyArray(),
CropToContent(),
PadToShape(self._normalized_shape),
ToNifti1Image(),
NiftiToDisk(
os.path.join(
os.path.join(
self._output_dir,
os.path.join("MRBrainS/Dual_Standardized",
root_dir_end)), file))
])
transforms_(os.path.join(root, file))
for root, dirs, files in os.walk(self._root_dir_iseg):
root_dir_end = os.path.basename(os.path.normpath(root))
if "ROI" in root_dir_end or "label" in root_dir_end:
for file in files:
if not os.path.exists(
os.path.join(
self._output_dir,
os.path.join("iSEG/Dual_Standardized",
root_dir_end))):
os.makedirs(
os.path.join(
self._output_dir,
os.path.join("iSEG/Dual_Standardized",
root_dir_end)))
transforms_ = transforms.Compose([
ToNumpyArray(),
CropToContent(),
PadToShape(self._normalized_shape),
ToNifti1Image(),
NiftiToDisk(
os.path.join(
os.path.join(
self._output_dir,
os.path.join("iSEG/Dual_Standardized",
root_dir_end)), file))
])
transforms_(os.path.join(root, file))
return np.sqrt(js_div)
if __name__ == '__main__':
model = Unet(1, 1, True, True)
iseg_csv = "/mnt/md0/Data/iSEG_scaled/Training/output_iseg_images.csv"
mrbrains_csv = "/mnt/md0/Data/MRBrainS_scaled/DataNii/TrainingData/output_mrbrains_images.csv"
abide_csv = "/mnt/md0/Data/ABIDE_scaled/output_abide_images.csv"
iseg_csv = pandas.read_csv(iseg_csv)
mrbrains_csv = pandas.read_csv(mrbrains_csv)
abide_csv = pandas.read_csv(abide_csv).sample(75)
c, d, h, w = 1, 256, 256, 192
transform = transforms.Compose([ToNumpyArray(), PadToShape((c, d, h, w))])
iseg_inputs = torch.tensor([transform(image) for image in iseg_csv["T1"]])
mrbrains_inputs = torch.tensor(
[transform(image) for image in mrbrains_csv["T1"]])
abide_inputs = torch.tensor(
[transform(image) for image in abide_csv["T1"]])
generated_iseg = transform(
"/mnt/md0/Research/DualUNet/Reconstructed_Normalized_iSEG_Image_80.nii.gz"
)
generated_mrbrains = transform(
"/mnt/md0/Research/DualUNet/Reconstructed_Normalized_MRBrainS_Image_80.nii.gz"
)
generated_abide = transform(
"/mnt/md0/Research/DualUNet/Reconstructed_Normalized_ABIDE_Image_80.nii.gz"
)
segmentation_iseg = transform(