def seg_target_lipiodol(P, thresholds=[75, 160, 215], num_tumors=1):
raise ValueError("Not ready")
ct_img, D = hf.nii_load(P["ct24"]["img"])
mask, _ = masks.get_mask(seg, D, ct_img.shape)
img = (mask > 0) * ct_img
#save_folder =
info[dirname(seg)] = (ct_img, D, mask)
if P["ct24"]["tumor"] is None:
tumor_mask = img > thresholds[1]
tumor_mask = binary_closing(tumor_mask)
else:
tumor_mask = masks.get_mask(P["ct24"]["tumor"], D, ct_img.shape)
mask1 = copy.deepcopy(img)
mask1 = mask1 > thresholds[1]
B2 = ball(2)
B2 = B2[:, :, [0, 2, 4]]
mask1 = binary_dilation(mask1, np.ones((3, 3, 1)))
if num_tumors == 1:
tumor_labels, num_labels = label(mask1, return_num=True)
label_sizes = [
np.sum(tumor_labels == label_id)
for label_id in range(1, num_labels + 1)
]
biggest_label = label_sizes.index(max(label_sizes)) + 1
mask1[tumor_labels != biggest_label] = 0
mask1 = binary_opening(binary_closing(mask1, structure=B2, iterations=2),
structure=B2,
iterations=2)
if num_tumors == 1:
tumor_labels, num_labels = label(mask1, return_num=True)
label_sizes = [
np.sum(tumor_labels == label_id)
for label_id in range(1, num_labels + 1)
]
biggest_label = label_sizes.index(max(label_sizes)) + 1
mask1[tumor_labels != biggest_label] = 0
target_mask = mask1 * tumor_mask
nontarget_mask = (1 - mask1) * tumor_mask
masks.save_mask(target_mask,
join(P["mask"], "target_lip"),
D,
save_mesh=True)
masks.save_mask(nontarget_mask,
join(P["mask"], "nontarget_lip"),
D,
save_mesh=True)
def seg_liver_mri(mri_img, save_path, mri_dims, model, tumor_mask_path=None):
"""Use a UNet to segment liver on MRI"""
C = config.Config()
#correct bias field!
orig_shape = mri_img.shape
x = mri_img
x -= np.amin(x)
x /= np.std(x)
crops = list(map(int,[.05 * x.shape[0], .95 * x.shape[0]] + \
[.05 * x.shape[1], .95 * x.shape[1]] + \
[.05 * x.shape[2], .95 * x.shape[2]]))
x = x[crops[0]:crops[1], crops[2]:crops[3], crops[4]:crops[5]]
scale_shape = x.shape
x = tr.rescale_img(x, C.dims)
y = model.predict(np.expand_dims(x, 0))[0]
liver_mask = (y[:, :, :, 1] > y[:, :, :, 0]).astype(float)
liver_mask = tr.rescale_img(liver_mask, scale_shape) #orig_shape)
liver_mask = np.pad(liver_mask, ((crops[0], orig_shape[0] - crops[1]),
(crops[2], orig_shape[1] - crops[3]),
(crops[4], orig_shape[2] - crops[5])),
'constant')
liver_mask = liver_mask > .5
B3 = ball(3)
B3 = B3[:, :, [0, 2, 3, 4, 6]]
#B3 = ball(4)
#B3 = B3[:,:,[1,3,5,6,8]]
liver_mask = binary_opening(binary_closing(liver_mask, B3, 1), B3, 1)
labels, num_labels = label(liver_mask, return_num=True)
label_sizes = [
np.sum(labels == label_id) for label_id in range(1, num_labels + 1)
]
biggest_label = label_sizes.index(max(label_sizes)) + 1
liver_mask[labels != biggest_label] = 0
if tumor_mask_path is not None:
tumor_mask, _ = masks.get_mask(tumor_mask_path, mri_dims,
mri_img.shape)
liver_mask[tumor_mask > tumor_mask.max() / 2] = liver_mask.max()
masks.save_mask(liver_mask, save_path, mri_dims, save_mesh=True)
def seg_liver_ct(ct_path, save_path, model, tumor_mask_path=None):
"""Use a UNet to segment liver on CT"""
C = config.Config()
ct_img, ct_dims = hf.nii_load(ct_path)
orig_shape = ct_img.shape
x = tr.apply_window(ct_img)
x -= np.amin(x)
crops = list(map(int,[.05 * x.shape[0], .95 * x.shape[0]] + \
[.05 * x.shape[1], .95 * x.shape[1]] + \
[.05 * x.shape[2], .95 * x.shape[2]]))
x = x[crops[0]:crops[1], crops[2]:crops[3], crops[4]:crops[5]]
scale_shape = x.shape
x = tr.rescale_img(x, C.dims)
y = model.predict(np.expand_dims(x, 0))[0]
liver_mask = (y[:, :, :, 1] > y[:, :, :, 0]).astype(float)
liver_mask[x < 30] = 0
liver_mask = tr.rescale_img(liver_mask, scale_shape)
liver_mask = np.pad(liver_mask, ((crops[0], orig_shape[0] - crops[1]),
(crops[2], orig_shape[1] - crops[3]),
(crops[4], orig_shape[2] - crops[5])),
'constant')
B3 = ball(3)
B3 = B3[:, :, [0, 2, 3, 4, 6]]
liver_mask = binary_opening(binary_closing(liver_mask, B3, 1), B3, 1)
labels, num_labels = label(liver_mask, return_num=True)
label_sizes = [
np.sum(labels == label_id) for label_id in range(1, num_labels + 1)
]
biggest_label = label_sizes.index(max(label_sizes)) + 1
liver_mask[labels != biggest_label] = 0
if tumor_mask_path is not None:
tumor_mask, _ = masks.get_mask(tumor_mask_path, ct_dims, ct_img.shape)
liver_mask[tumor_mask > tumor_mask.max() / 2] = liver_mask.max()
masks.save_mask(liver_mask, save_path, ct_dims, save_mesh=True)
def seg_lipiodol(P, thresholds=[75, 160, 215]):
#P = get_paths_dict(lesion_id, target_dir)
img, dims = hf.nii_load(P['ct24']['img'])
low_mask = copy.deepcopy(img)
low_mask = low_mask > thresholds[0]
low_mask = binary_closing(binary_opening(low_mask,
structure=np.ones((3, 3, 2))),
structure=np.ones((2, 2, 1))) #2,2,1
mid_mask = copy.deepcopy(img)
mid_mask = mid_mask > thresholds[1]
mid_mask = binary_closing(mid_mask)
high_mask = copy.deepcopy(img)
high_mask = high_mask > thresholds[2]
mask, _ = masks.get_mask(P['ct24']['liver'], dims, img.shape)
low_mask = low_mask * mask
mid_mask = mid_mask * mask
high_mask = high_mask * mask
masks.save_mask(low_mask, P['ct24']['lowlip'], dims, save_mesh=True)
masks.save_mask(mid_mask, P['ct24']['midlip'], dims, save_mesh=True)
masks.save_mask(high_mask, P['ct24']['highlip'], dims, save_mesh=True)
return low_mask, mid_mask, high_mask
def seg_tumor_ct(P, threshold=150, num_tumors=1):
ct_img, D = hf.nii_load(P["ct24"]["img"])
mask, _ = masks.get_mask(P["ct24"]["liver"], D, ct_img.shape)
tumor_mask = (mask > 0) * ct_img > threshold
tumor_mask = binary_closing(tumor_mask)
if num_tumors == 1:
tumor_labels, num_labels = label(tumor_mask, return_num=True)
label_sizes = [
np.sum(tumor_labels == label_id)
for label_id in range(1, num_labels + 1)
]
biggest_label = label_sizes.index(max(label_sizes)) + 1
tumor_mask[tumor_labels != biggest_label] = 0
B3 = ball(3)
B3 = B3[:, :, [0, 2, 3, 4, 6]]
tumor_mask = binary_opening(binary_closing(mask1, B3), B3)
masks.save_mask(tumor_mask, P["ct24"]["tumor"], D)
return tumor_mask
def reg_to_ct24(lesion_id, target_dir, D=[1., 1., 2.5], padding=.2):
importlib.reload(reg)
P = get_paths_dict(lesion_id, target_dir)
ct24, ct24_dims = hf.nii_load(P['ct24']['img'])
fmod = 'ct24'
mod = 'mrbl'
xform_path, crops, pad_m = reg.get_mask_Tx(P[fmod]['img'],
P[fmod]['tumor'],
P[mod]['art'],
P[mod]['tumor'],
padding=padding,
D=D)
crop_ct24 = hf.crop_nonzero(ct24, crops[1])[0]
t_shape = crop_ct24.shape
reg.transform_region(P[mod]['art'],
xform_path,
crops,
pad_m,
ct24_dims,
P['ct24Tx'][mod]['art'],
target_shape=t_shape,
D=D)
reg.transform_region(P[mod]['sub'],
xform_path,
crops,
pad_m,
ct24_dims,
P['ct24Tx'][mod]['sub'],
target_shape=t_shape,
D=D)
#reg.transform_region(P[mod]['equ'], xform_path, crops, pad_m, ct24_dims,
# P['ct24Tx'][mod]['equ'], target_shape=t_shape, D=D);
if exists(P[mod]['enh'] + ".off"):
reg.transform_mask(P[mod]['enh'],
P[mod]['art'],
xform_path,
crops,
pad_m,
ct24_dims,
P['ct24Tx'][mod]['enh'],
target_shape=t_shape,
D=D)
hf.save_nii(crop_ct24, P['ct24Tx']['crop']['img'], ct24_dims)
M = masks.get_mask(P['ct24']['tumor'], ct24_dims, ct24.shape)[0]
M = hf.crop_nonzero(M, crops[1])[0]
masks.save_mask(M, P['ct24Tx']['crop']['tumor'], ct24_dims)
mod = 'mr30'
xform_path, crops, pad_m = reg.get_mask_Tx(P[fmod]['img'],
P[fmod]['tumor'],
P[mod]['art'],
P[mod]['tumor'],
padding=padding,
D=D)
reg.transform_region(P[mod]['art'],
xform_path,
crops,
pad_m,
ct24_dims,
P['ct24Tx'][mod]['art'],
target_shape=t_shape,
D=D)
if exists(P[mod]['enh'] + ".off"):
reg.transform_mask(P[mod]['enh'],
P[mod]['art'],
xform_path,
crops,
pad_m,
ct24_dims,
P['ct24Tx'][mod]['enh'],
target_shape=t_shape,
D=D)
