def process(self, sample):
augment_p = sample_augmentation_parameters(self.augmentation_params)
for tag in self.tags:
pixelspacingtag = tag.split(':')[0] + ":pixelspacing"
assert pixelspacingtag in sample[
INPUT], "tag %s not found" % pixelspacingtag
spacing = sample[INPUT][pixelspacingtag]
volume = sample[INPUT][tag]
new_vol = np.zeros((self.max_rois, ) + self.output_shape,
volume.dtype)
patient_id = sample[INPUT][tag.split(':')[0] + ":patient_id"]
rois = self.rois[patient_id]
np.random.shuffle(rois)
for i in range(min(len(rois), self.max_rois)):
# mm to input space
center_to_shift = -rois[i] / np.asarray(spacing, np.float)
# print rois[i], center_to_shift
new_vol[i] = augment_3d(volume=volume,
pixel_spacing=spacing,
output_shape=self.output_shape,
norm_patch_shape=self.norm_patch_shape,
augment_p=augment_p,
center_to_shift=center_to_shift)
sample[INPUT][tag] = new_vol # shape: (max_rois, X, Y, Z)
def process(self, sample):
augment_p = sample_augmentation_parameters(self.augmentation_params)
for tag in self.tags:
pixelspacingtag = tag.split(':')[0] + ":pixelspacing"
labelstag = tag.split(':')[0] + ":labels"
origintag = tag.split(':')[0] + ":origin"
assert pixelspacingtag in sample[
INPUT], "tag %s not found" % pixelspacingtag
assert labelstag in sample[INPUT], "tag %s not found" % labelstag
assert origintag in sample[INPUT], "tag %s not found" % origintag
spacing = sample[INPUT][pixelspacingtag]
labels = sample[INPUT][labelstag]
origin = sample[INPUT][origintag]
label = random.choice(labels)
from application.luna import LunaDataLoader
labelloc = LunaDataLoader.world_to_voxel_coordinates(
label[:3], origin=origin, spacing=spacing)
if tag in sample[INPUT]:
volume = sample[INPUT][tag]
sample[INPUT][tag] = augment_3d(
volume=volume,
pixel_spacing=spacing,
output_shape=self.output_shape,
norm_patch_shape=self.norm_patch_shape,
augment_p=augment_p,
center_to_shift=-labelloc)
elif tag in sample[OUTPUT]:
volume = sample[OUTPUT][tag]
sample[OUTPUT][tag] = augment_3d(
volume=volume,
pixel_spacing=spacing,
output_shape=self.output_shape,
norm_patch_shape=self.norm_patch_shape,
augment_p=augment_p,
center_to_shift=-labelloc,
cval=0.0)
else:
pass
def process(self, sample):
augment_p = sample_augmentation_parameters(self.augmentation_params)
tag = self.tags[0]
basetag = tag.split(':')[0]
pixelspacingtag = basetag + ":pixelspacing"
patient_idtag = basetag + ":patient_id"
origintag = basetag + ":origin"
spacing = sample[INPUT][pixelspacingtag]
patient_id = sample[INPUT][patient_idtag]
candidates = self.candidates[patient_id]
origin = sample[INPUT][origintag]
if len(candidates) == 1:
candidate = random.choice(candidates[0])
elif len(candidates) == 2:
percentage_chance = 0.5
if random.random() < percentage_chance:
candidate = random.choice(candidates[1])
else:
candidate = random.choice(candidates[0])
else:
raise Exception("candidates is empty")
#print 'candidate', candidate
candidateloc = LunaDataLoader.world_to_voxel_coordinates(
candidate[:3], origin=origin, spacing=spacing)
volume = sample[INPUT][basetag + ":3d"]
sample[INPUT][basetag + ":3d"] = augment_3d(
volume=volume,
pixel_spacing=spacing,
output_shape=self.output_shape,
norm_patch_shape=self.norm_patch_shape,
augment_p=augment_p,
center_to_shift=-candidateloc)
# add candidate label to output tags
sample[OUTPUT][basetag + ":target"] = np.int32(candidate[3])
def process(self, sample):
augment_p = sample_augmentation_parameters(self.augmentation_params)
for tag in self.tags:
pixelspacingtag = tag.split(':')[0] + ":pixelspacing"
assert pixelspacingtag in sample[INPUT], "tag %s not found" % pixelspacingtag
spacing = sample[INPUT][pixelspacingtag]
volume = sample[INPUT][tag]
new_vol = np.zeros((self.max_rois,)+self.output_shape, volume.dtype)
patient_id = sample[INPUT][tag.split(':')[0] + ":patient_id"]
d_rois = self.rois[patient_id]
print 'loaded rois from patient', patient_id
rois = d_rois['rois']
in_mask = d_rois['in_mask']
fpr_p = d_rois['fpr_p']
fpr_p[in_mask==0] = 0.
rank_rois = len(fpr_p)-rankdata(fpr_p).astype(int)
top = rois[rank_rois<self.max_rois]
np.random.shuffle(top)
for idx, roi in enumerate(top):
# mm to input space
center_to_shift = -rois[idx]/np.asarray(spacing, np.float)
# print rois[i], center_to_shift
new_vol[idx] = augment_3d(
volume=volume,
pixel_spacing=spacing,
output_shape=self.output_shape,
norm_patch_shape=self.norm_patch_shape,
augment_p=augment_p,
center_to_shift=center_to_shift
)
sample[INPUT][tag] = new_vol # shape: (max_rois, X, Y, Z)