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fix(nnUNet): Correct background mask in BraTS preprocessor #1457

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fix(nnUNet): Correct background mask in BraTS preprocessor #1457

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69 changes: 55 additions & 14 deletions PyTorch/Segmentation/nnUNet/data_preprocessing/preprocessor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,15 @@
from skimage.transform import resize
from utils.utils import get_task_code, make_empty_dir

from data_preprocessing.configs import ct_max, ct_mean, ct_min, ct_std, patch_size, spacings, task
from data_preprocessing.configs import (
ct_max,
ct_mean,
ct_min,
ct_std,
patch_size,
spacings,
task,
)


class Preprocessor:
Expand All @@ -45,9 +53,15 @@ def __init__(self, args):
self.ct_min, self.ct_max, self.ct_mean, self.ct_std = (0,) * 4
if not self.training:
self.results = os.path.join(self.results, self.args.exec_mode)
self.crop_foreg = transforms.CropForegroundd(keys=["image", "label"], source_key="image")
nonzero = True if self.modality != "CT" else False # normalize only non-zero region for MRI
self.normalize_intensity = transforms.NormalizeIntensity(nonzero=nonzero, channel_wise=True)
self.crop_foreg = transforms.CropForegroundd(
keys=["image", "label"], source_key="image"
)
nonzero = (
True if self.modality != "CT" else False
) # normalize only non-zero region for MRI
self.normalize_intensity = transforms.NormalizeIntensity(
nonzero=nonzero, channel_wise=True
)
if self.args.exec_mode == "val":
dataset_json = json.load(open(metadata_path, "r"))
dataset_json["val"] = dataset_json["training"]
Expand Down Expand Up @@ -76,7 +90,9 @@ def run(self):
_mean = round(self.ct_mean, 2)
_std = round(self.ct_std, 2)
if self.verbose:
print(f"[CT] min: {self.ct_min}, max: {self.ct_max}, mean: {_mean}, std: {_std}")
print(
f"[CT] min: {self.ct_min}, max: {self.ct_max}, mean: {_mean}, std: {_std}"
)

self.run_parallel(self.preprocess_pair, self.args.exec_mode)

Expand Down Expand Up @@ -114,7 +130,7 @@ def preprocess_pair(self, pair):
if self.args.ohe:
mask = np.ones(image.shape[1:], dtype=np.float32)
for i in range(image.shape[0]):
zeros = np.where(image[i] <= 0)
zeros = np.where(image[i] == 0)
mask[zeros] *= 0.0
image = self.normalize_intensity(image).astype(np.float32)
mask = np.expand_dims(mask, 0)
Expand All @@ -131,15 +147,28 @@ def standardize(self, image, label):
pad_shape = self.calculate_pad_shape(image)
image_shape = image.shape[1:]
if pad_shape != image_shape:
paddings = [(pad_sh - image_sh) / 2 for (pad_sh, image_sh) in zip(pad_shape, image_shape)]
paddings = [
(pad_sh - image_sh) / 2
for (pad_sh, image_sh) in zip(pad_shape, image_shape)
]
image = self.pad(image, paddings)
label = self.pad(label, paddings)
if self.args.dim == 2: # Center cropping 2D images.
_, _, height, weight = image.shape
start_h = (height - self.patch_size[0]) // 2
start_w = (weight - self.patch_size[1]) // 2
image = image[:, :, start_h : start_h + self.patch_size[0], start_w : start_w + self.patch_size[1]]
label = label[:, :, start_h : start_h + self.patch_size[0], start_w : start_w + self.patch_size[1]]
image = image[
:,
:,
start_h : start_h + self.patch_size[0],
start_w : start_w + self.patch_size[1],
]
label = label[
:,
:,
start_h : start_h + self.patch_size[0],
start_w : start_w + self.patch_size[1],
]
return image, label

def normalize(self, image):
Expand All @@ -148,7 +177,9 @@ def normalize(self, image):
return self.normalize_intensity(image)

def save(self, image, label, fname, image_metadata):
mean, std = np.round(np.mean(image, (1, 2, 3)), 2), np.round(np.std(image, (1, 2, 3)), 2)
mean, std = np.round(np.mean(image, (1, 2, 3)), 2), np.round(
np.std(image, (1, 2, 3)), 2
)
if self.verbose:
print(f"Saving {fname} shape {image.shape} mean {mean} std {std}")
self.save_npy(image, fname, "_x.npy")
Expand Down Expand Up @@ -191,7 +222,9 @@ def calculate_pad_shape(self, image):
image_shape = image.shape[1:]
if len(min_shape) == 2: # In 2D case we don't want to pad depth axis.
min_shape.insert(0, image_shape[0])
pad_shape = [max(mshape, ishape) for mshape, ishape in zip(min_shape, image_shape)]
pad_shape = [
max(mshape, ishape) for mshape, ishape in zip(min_shape, image_shape)
]
return pad_shape

def get_intensities(self, pair):
Expand Down Expand Up @@ -233,10 +266,16 @@ def calculate_new_shape(self, spacing, shape):
return new_shape

def save_npy(self, image, fname, suffix):
np.save(os.path.join(self.results, fname.replace(".nii.gz", suffix)), image, allow_pickle=False)
np.save(
os.path.join(self.results, fname.replace(".nii.gz", suffix)),
image,
allow_pickle=False,
)

def run_parallel(self, func, exec_mode):
return Parallel(n_jobs=self.args.n_jobs)(delayed(func)(pair) for pair in self.metadata[exec_mode])
return Parallel(n_jobs=self.args.n_jobs)(
delayed(func)(pair) for pair in self.metadata[exec_mode]
)

def load_nifty(self, fname):
return nibabel.load(os.path.join(self.data_path, fname))
Expand Down Expand Up @@ -266,7 +305,9 @@ def standardize_layout(data):

@staticmethod
def resize_fn(image, shape, order, mode):
return resize(image, shape, order=order, mode=mode, cval=0, clip=True, anti_aliasing=False)
return resize(
image, shape, order=order, mode=mode, cval=0, clip=True, anti_aliasing=False
)

def resample_anisotrophic_image(self, image, shape):
resized_channels = []
Expand Down