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import lungmask
from lungmask import mask
from monai import transforms
from monai.transforms.intensity.array import ThresholdIntensity
from monai.transforms.spatial.array import Resize, Spacing
from monai.transforms.utility.dictionary import ToTensord
import torch
import numpy as np
from monai.transforms import (Compose, LoadImaged, ToNumpyd, ThresholdIntensityd, AddChanneld, NormalizeIntensityd, SpatialCropd, DivisiblePadd, Spacingd, SqueezeDimd)
def mask_lung(scan_path, batch_size=20):
model = lungmask.mask.get_model('unet', 'R231')
device = torch.device('cuda')
model.to(device)
scan_dict = {
'image' : scan_path
}
transformer = Compose(
[
LoadImaged(keys=['image']),
ToNumpyd(keys=['image']),
]
)
scan_read = transformer(scan_dict)
inimg_raw = scan_read['image'].swapaxes(0,2)
tvolslices, xnew_box = lungmask.utils.preprocess(inimg_raw, resolution=[256, 256])
tvolslices[tvolslices > 600] = 600
tvolslices = np.divide((tvolslices + 1024), 1624)
torch_ds_val = lungmask.utils.LungLabelsDS_inf(tvolslices)
dataloader_val = torch.utils.data.DataLoader(torch_ds_val, batch_size=batch_size, shuffle=False, num_workers=1,
pin_memory=False)
timage_res = np.empty((np.append(0, tvolslices[0].shape)), dtype=np.uint8)
with torch.no_grad():
for X in dataloader_val:
X = X.float().to(device)
prediction = model(X)
pls = torch.max(prediction, 1)[1].detach().cpu().numpy().astype(np.uint8)
timage_res = np.vstack((timage_res, pls))
outmask = lungmask.utils.postrocessing(timage_res)
outmask = np.asarray(
[lungmask.utils.reshape_mask(outmask[i], xnew_box[i], inimg_raw.shape[1:]) for i in range(outmask.shape[0])],
dtype=np.uint8)
outmask = np.swapaxes(outmask, 0, 2)
#outmask = np.flip(outmask, 0)
return outmask.astype(np.uint8), scan_read['image_meta_dict']['affine']
def calculate_extremes(image, annotation_value):
holder = np.copy(image)
x_min = float('inf')
x_max = 0
y_min = float('inf')
y_max = 0
z_min = -1
z_max = 0
holder[holder != annotation_value] = 0
holder = np.swapaxes(holder, 0, 2)
for i, layer in enumerate(holder):
if(np.amax(layer) < 1):
continue
if(z_min == -1):
z_min = i
z_max = i
y = np.any(layer, axis = 1)
x = np.any(layer, axis = 0)
y_minl, y_maxl = np.argmax(y) + 1, layer.shape[0] - np.argmax(np.flipud(y))
x_minl, x_maxl = np.argmax(x) + 1, layer.shape[1] - np.argmax(np.flipud(x))
if(y_minl < y_min):
y_min = y_minl
if(x_minl < x_min):
x_min = x_minl
if(y_maxl > y_max):
y_max = y_maxl
if(x_maxl > x_max):
x_max = x_maxl
return ((x_min, x_max), (y_min, y_max), (z_min, z_max))
def process_lung_scan(scan_dict, extremes):
load_transformer = Compose(
[
LoadImaged(keys=["image"]),
ThresholdIntensityd(keys=['image'], above = False, threshold = 1000, cval = 1000),
ThresholdIntensityd(keys=['image'], above = True, threshold = -1024, cval = -1024),
AddChanneld(keys=["image"]),
NormalizeIntensityd(keys=["image"]),
SpatialCropd(keys=["image"], roi_start=(extremes[0][0], extremes[1][0], extremes[2][0]), roi_end=(extremes[0][1], extremes[1][1], extremes[2][1])),
Spacingd(keys=["image"], pixdim=(1, 1, 1.5)),
]
)
processed_1 = load_transformer(scan_dict)
transformer_1 = Compose(
[
DivisiblePadd(keys=["image"], k=16, mode='constant'),
ToTensord(keys=['image'])
#SqueezeDimd(keys=["image"], dim = 0),
#ToNumpyd(keys=["image"]),
]
)
processed_2 = transformer_1(processed_1)
affine = processed_2['image_meta_dict']['affine']
normalized_image = processed_2['image']
return normalized_image, affine
def preprocess(image_path):
preprocess_dump = {}
scan_dict = {
'image' : image_path
}
im = LoadImaged(keys=['image'])(scan_dict)
preprocess_dump['org_shape'] = im['image'].shape
preprocess_dump['pixdim'] = im['image_meta_dict']['pixdim'][1:4]
preprocess_dump['org_affine'] = im['image_meta_dict']['affine']
masked_lungs = mask_lung(image_path, 5)
right_lung_extreme = calculate_extremes(masked_lungs[0], 1)
preprocess_dump['right_extremes'] = right_lung_extreme
right_lung_processed = process_lung_scan(scan_dict, right_lung_extreme)
left_lung_extreme = calculate_extremes(masked_lungs[0], 2)
preprocess_dump['left_extremes'] = left_lung_extreme
left_lung_processed = process_lung_scan(scan_dict, left_lung_extreme)
preprocess_dump['affine'] = left_lung_processed[1]
preprocess_dump['right_lung'] = right_lung_processed[0].unsqueeze(0)
preprocess_dump['left_lung'] = left_lung_processed[0].unsqueeze(0)
return preprocess_dump
def find_pad_edge(original):
a_min = -1
a_max = original.shape[0]
for i in range(len(original)):
a_min = i
if(np.any(original[i])):
break
for i in range(len(original) - 1, 0, -1):
a_max = i
if(np.any(original[i])):
break
original = original.swapaxes(0,1)
b_min = -1
b_max = original.shape[0]
for i in range(len(original)):
b_min = i
if(np.any(original[i])):
break
for i in range(len(original) - 1, 0, -1):
b_max = i
if(np.any(original[i])):
break
original = original.swapaxes(0,1)
original = original.swapaxes(0,2)
c_min = -1
c_max = original.shape[0]
for i in range(len(original)):
c_min = i
if(np.any(original[i])):
break
for i in range(len(original) - 1, 0, -1):
c_max = i
if(np.any(original[i])):
break
return a_min, a_max + 1, b_min, b_max + 1, c_min, c_max + 1
def remove_pad(mask, original):
a_min, a_max, b_min, b_max, c_min, c_max = find_pad_edge(original)
return mask[a_min:a_max, b_min:b_max, c_min: c_max]
def voxel_space(image, target):
image = Resize((target[0][1]-target[0][0], target[1][1]-target[1][0], target[2][1]-target[2][0]), mode='trilinear')(np.expand_dims(image, 0))[0]
image = ThresholdIntensity(above = False, threshold = 0.5, cval = 1)(image)
image = ThresholdIntensity(above = True, threshold = 0.5, cval = 0)(image)
return image
def stitch(org_shape, cropped, roi):
holder = np.zeros(org_shape)
holder[roi[0][0]:roi[0][1], roi[1][0]:roi[1][1], roi[2][0]:roi[2][1]] = cropped
return holder
def post_process(left_mask, right_mask, preprocess_dump):
left_mask[left_mask >= 0.5] = 1
left_mask[left_mask < 0.5] = 0
left_mask = left_mask.astype(int)
right_mask[right_mask >= 0.5] = 1
right_mask[right_mask < 0.5] = 0
right_mask = right_mask.astype(int)
left = remove_pad(left_mask, preprocess_dump['left_lung'].squeeze(0).squeeze(0).numpy())
right = remove_pad(right_mask, preprocess_dump['right_lung'].squeeze(0).squeeze(0).numpy())
left = voxel_space(left, preprocess_dump['left_extremes'])
right = voxel_space(right, preprocess_dump['right_extremes'])
left = stitch(preprocess_dump['org_shape'], left, preprocess_dump['left_extremes'])
right = stitch(preprocess_dump['org_shape'], right, preprocess_dump['right_extremes'])
stitched = np.logical_or(left, right).astype(int)
return stitched |