def pre_processing(dict_images):
MR = dict_images['MR']
MR = np.clip(MR / 2048, a_min=0, a_max=1)
Mask = dict_images['Mask']
_, D, H, W = MR.shape
heatmap_generator = HeatmapGenerator(image_size=(D, H, W),
sigma=2.,
scale_factor=1.,
normalize=True,
size_sigma_factor=8,
sigma_scale_factor=2,
dtype=np.float32)
list_landmarks = dict_images['list_landmarks']
index = random.randint(10, 18)
while True in np.isnan(list_landmarks[index]):
index = random.randint(10, 18)
heatmap = heatmap_generator.generate_heatmap(landmark=list_landmarks[index])[np.newaxis, :, :, :]
Mask = np.where(Mask == index + 1, 1, 0) # just segment one IVD
if D > 12:
start = random.choice([i for i in range(D - 12 + 1)])
MR = crop(MR, start=start, end=start + 12, axis='z')
heatmap = crop(heatmap, start=start, end=start + 12, axis='z')
Mask = crop(Mask, start=start, end=start + 12, axis='z')
MR = crop_to_center(MR, list_landmarks[index], dsize=(12, 64, 96))
heatmap = crop_to_center(heatmap, list_landmarks[index], dsize=(12, 64, 96))
Mask = crop_to_center(Mask, list_landmarks[index], dsize=(12, 64, 96))
return [np.concatenate((MR, heatmap)), Mask]
def crop_to_center(img, landmark=(0, 0, 0), dsize=(12, 128, 128)):
"""
:param img 4D image with shape (C, D, H, W)
"""
_, D, H, W = img.shape
# bz = max(landmark[0] - dsize[0] // 2, 0)
# ez = min(bz + dsize[0], D)
pad_h_1, pad_h_2, pad_w_1, pad_w_2 = 0, 0, 0, 0
bh = landmark[1] - dsize[1] // 2
eh = bh + dsize[1]
if bh < 0:
pad_h_1 = abs(bh)
bh = 0
if eh > H:
pad_h_2 = eh - H
eh = H
bw = landmark[2] - dsize[2] // 2
ew = bw + dsize[2]
if bw < 0:
pad_w_1 = abs(bw)
bw = 0
if ew > W:
pad_w_2 = ew - W
ew = W
# img = crop(img, bz, ez, axis='z')
img = crop(img, bh, eh, axis='y')
img = crop(img, bw, ew, axis='x')
img = np.pad(img,
((0, 0), (0, 0), (pad_h_1, pad_h_2), (pad_w_1, pad_w_2)),
mode='constant',
constant_values=0)
return img
def pre_processing(dict_images):
MR = dict_images['MR']
MR = np.clip(MR / 2048, a_max=1, a_min=0)
_, D, H, W = MR.shape
heatmap_generator = HeatmapGenerator(image_size=(D, H, W),
sigma=2.,
spine_heatmap_sigma=20,
scale_factor=3.,
spine_heatmap_scale_factor=10.,
normalize=True,
size_sigma_factor=8,
sigma_scale_factor=2,
dtype=np.float32)
spine_heatmap = heatmap_generator.generate_spine_heatmap(
list_landmarks=dict_images['list_landmarks'])
if D > 12:
start = random.choice([i for i in range(D - 12 + 1)])
MR = crop(MR, start=start, end=start + 12, axis='z')
spine_heatmap = crop(spine_heatmap,
start=start,
end=start + 12,
axis='z')
return [MR, spine_heatmap]
def pre_processing(dict_images):
MR = dict_images['MR']
_, D, H, W = MR.shape
MR = crop(MR, start=int(W / 4), end=-int(W / 4), axis='W')
MR = normalize(MR)
Mask = dict_images['Mask']
Mask = crop(Mask, start=int(W / 4), end=-int(W / 4), axis='W')
# raw_Mask = dict_images['raw_Mask']
list_images = [MR, Mask] # (C, D, H, W) or (C, z, y, x)
return list_images
def pre_processing(dict_images):
MR = dict_images['MR']
# MR = np.clip(MR, a_min=-1024)
_, D, H, W = MR.shape
MR = crop(MR, start=int(W / 4), end=-int(W / 4), axis='W')
MR = normalize(MR) # normalization
# Mask = dict_images['Mask']
list_images = [MR]
return list_images
def pre_processing(dict_images):
MR = dict_images['MR']
MR = np.clip(MR / 2048, a_max=1, a_min=0)
_, D, H, W = MR.shape
heatmap_generator = HeatmapGenerator(image_size=(D, H, W),
sigma=2.,
spine_heatmap_sigma=20,
scale_factor=1.,
normalize=True,
size_sigma_factor=6,
sigma_scale_factor=1,
dtype=np.float32)
spine_heatmap = heatmap_generator.generate_spine_heatmap(
list_landmarks=dict_images['list_landmarks'])
heatmaps = heatmap_generator.generate_heatmaps(
list_landmarks=dict_images['list_landmarks'])
centroid_coordinate = [
round(i) for i in ndimage.center_of_mass(spine_heatmap)
] # (0, z, y, x)
start_x = centroid_coordinate[-1] - W // 4
end_x = centroid_coordinate[-1] + W // 4
MR = crop(MR, start=start_x, end=end_x, axis='x')
spine_heatmap = crop(spine_heatmap, start=start_x, end=end_x, axis='x')
heatmaps = crop(heatmaps, start_x, end=end_x, axis='x')
if D > 12:
start_z = random.choice([i for i in range(D - 12 + 1)])
MR = crop(MR, start=start_z, end=start_z + 12, axis='z')
spine_heatmap = crop(spine_heatmap,
start=start_z,
end=start_z + 12,
axis='z')
heatmaps = crop(heatmaps, start_z, end=start_z + 12, axis='z')
# FIXME crop patches
start_y = random.choice((0, H // 4, H // 2))
end_y = start_y + H // 2
MR = crop(MR, start=start_y, end=end_y, axis='y')
spine_heatmap = crop(spine_heatmap, start=start_y, end=end_y, axis='y')
heatmaps = crop(heatmaps, start_y, end=end_y, axis='y')
return [MR, spine_heatmap, heatmaps
] # (1, 12, 256, 256), (1, 12, 256, 256), (19, 12, 256, 256)