def draw_mrseq_with_mask(lesion_id, target_dir, save_dir, mod='mrbl'):
importlib.reload(masks)
P = lm.get_paths_dict(lesion_id, target_dir)
out_img = []
art, C = masks.crop_img_to_mask_vicinity(P[mod]['art'],
P[mod]['tumor'],
.5,
return_crops=True)
pre = masks.crop_img_to_mask_vicinity(P[mod]['pre'], P[mod]['tumor'], .5)
equ = masks.crop_img_to_mask_vicinity(P[mod]['equ'], P[mod]['tumor'], .5)
sub = art - pre
sl = art.shape[-1] // 2
I, D = hf.nii_load(P[mod]['art'])
if exists(P[mod]['enh'] + ".off"):
mask = masks.get_mask(P[mod]['enh'], D, I.shape)
mask = hf.crop_nonzero(mask, C)[0]
else:
mask = np.zeros(art.shape)
tumor_mask = masks.get_mask(P[mod]['tumor'], D, I.shape)
tumor_mask = hf.crop_nonzero(tumor_mask, C)[0]
sub_w_mask = vis.create_contour_img(sub, [tumor_mask, mask])
vis.display_sequence([
pre[..., sl], art[..., sl], equ[..., sl], sub[..., sl], sub_w_mask,
mask[..., sl]
], 2, 3, join(save_dir, "%s_%s.png" % (lesion_id, mod)))
def spherize(lesion_id, target_dir, R=1.):
importlib.reload(reg)
def ball_ct_batch():
reg.transform_region(P['ct24']['img'],
xform_path,
crops,
pads, [R] * 3,
P['ball']['ct24']['img'],
intermed_shape=ball_shape)
#if exists(P['ct24']['midlip']+".off"):
# reg.transform_mask(P['ct24']['midlip'], P['ct24']['img'], xform_path,
# crops, pads, [R]*3, P['ball']['ct24']['midlip'], intermed_shape=ball_shape)
# if exists(P['ct24']['highlip']+".off"):
# reg.transform_mask(P['ct24']['highlip'], P['ct24']['img'], xform_path,
# crops, pads, [R]*3, P['ball']['ct24']['highlip'], intermed_shape=ball_shape)
def ball_mr_batch(mod):
reg.transform_region(P[mod]['art'],
xform_path,
crops,
pads, [R] * 3,
P['ball'][mod]['art'],
intermed_shape=ball_shape)
if exists(P['ball'][mod]['enh'] + ".off"):
reg.transform_mask(P[mod]['enh'],
P[mod]['art'],
xform_path,
crops,
pads, [R] * 3,
P['ball'][mod]['enh'],
intermed_shape=ball_shape)
P = get_paths_dict(lesion_id, target_dir)
ctmask, ctd = masks.get_mask(P['ct24']['tumor'], img_path=P['ct24']['img'])
mrmask, mrd = masks.get_mask(P['mrbl']['tumor'], img_path=P['mrbl']['art'])
ctmask = hf.crop_nonzero(ctmask)[0]
mrmask = hf.crop_nonzero(mrmask)[0]
CT = np.max([ctmask.shape[i] * ctd[i] for i in range(3)])
MRBL = np.max([mrmask.shape[i] * mrd[i] for i in range(3)])
mrmask, mrd = masks.get_mask(P['mr30']['tumor'], img_path=P['mr30']['art'])
mrmask = hf.crop_nonzero(mrmask)[0]
MR30 = np.max([mrmask.shape[i] * mrd[i] for i in range(3)])
if CT > MRBL and CT > MR30:
xform_path, crops, pads = reg.get_mask_Tx_shape(
P['ct24']['img'], P['ct24']['tumor'], mask_path=P['ball']['mask'])
ball_shape = masks.get_mask(P['ball']['mask'])[0].shape
ball_ct_batch()
xform_path, crops, pads = reg.get_mask_Tx_shape(
P['mrbl']['art'],
P['mrbl']['tumor'],
ball_mask_path=P['ball']['mask'])
ball_mr_batch('mrbl')
xform_path, crops, pads = reg.get_mask_Tx_shape(
P['mr30']['art'],
P['mr30']['tumor'],
ball_mask_path=P['ball']['mask'])
ball_mr_batch('mr30')
elif MRBL > MR30:
xform_path, crops, pads = reg.get_mask_Tx_shape(
P['mrbl']['art'], P['mrbl']['tumor'], mask_path=P['ball']['mask'])
ball_shape = masks.get_mask(P['ball']['mask'])[0].shape
ball_mr_batch('mrbl')
xform_path, crops, pads = reg.get_mask_Tx_shape(
P['ct24']['img'],
P['ct24']['tumor'],
ball_mask_path=P['ball']['mask'])
ball_ct_batch()
xform_path, crops, pads = reg.get_mask_Tx_shape(
P['mr30']['art'],
P['mr30']['tumor'],
ball_mask_path=P['ball']['mask'])
ball_mr_batch('mr30')
else:
xform_path, crops, pads = reg.get_mask_Tx_shape(
P['mr30']['art'], P['mr30']['tumor'], mask_path=P['ball']['mask'])
ball_shape = masks.get_mask(P['ball']['mask'])[0].shape
ball_mr_batch('mr30')
xform_path, crops, pads = reg.get_mask_Tx_shape(
P['mrbl']['art'],
P['mrbl']['tumor'],
ball_mask_path=P['ball']['mask'])
ball_mr_batch('mrbl')
xform_path, crops, pads = reg.get_mask_Tx_shape(
P['ct24']['img'],
P['ct24']['tumor'],
ball_mask_path=P['ball']['mask'])
ball_ct_batch()
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)