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from lungmask import mask
import lungmask
import SimpleITK as sitk
import numpy as np
import nibabel
import platform
import glob
import torch
import skimage
from tqdm import tqdm
from monai.transforms import Compose, LoadImaged, ToTensord, Spacingd, DivisiblePadd, SpatialCropd, ToNumpyd, AddChanneld, SqueezeDimd, Resized, Flipd, Rotate90d, NormalizeIntensityd, ThresholdIntensityd
from Logger.loggingservice import Logger
logger = Logger("http://82.194.207.154:5000/api/log", "XqnJHdalUd")
directory_split = "\\" if platform.system() == "Windows" else "/"
def find_probable_air_value(image):
holder = np.copy(image)
min_val = np.amin(holder)
holder[holder == min_val] = float('inf')
return np.amin(holder), min_val
def mask_lung(scan_dict, batch_size=20):
model = lungmask.mask.get_model('unet', 'R231')
device = torch.device('cuda')
model.to(device)
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 tqdm(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 segment_lung(image_path):
sitk_image = sitk.ReadImage(image_path)
segmentation = mask.apply(sitk_image, batch_size = 5, model = mask.get_model('unet', 'R231'))
return segmentation
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, save_directory, extremes, lung):
load_transformer = Compose(
[
LoadImaged(keys=["image", "label", "boxes"]),
ThresholdIntensityd(keys=['image'], above = False, threshold = 1000, cval = 1000),
ThresholdIntensityd(keys=['image'], above = True, threshold = -1024, cval = -1024),
AddChanneld(keys=["image", "label", "boxes"]),
NormalizeIntensityd(keys=["image"]),
SpatialCropd(keys=["image", "label", "boxes"], 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)
if(np.amax(processed_1['label'][0]) == 0):
return
transformer_1 = Compose(
[
Resized(keys=["label", "boxes"], spatial_size=processed_1['image'].shape[1:]),
ThresholdIntensityd(keys=['boxes', 'label'], above = False, threshold = 0.5, cval = 1),
ThresholdIntensityd(keys=['boxes', 'label'], above = True, threshold = 0.5, cval = 0),
DivisiblePadd(keys=["image", "label", "boxes"], k=16, mode='symmetric'),
SqueezeDimd(keys=["image", "label", "boxes"], dim = 0),
ToNumpyd(keys=["image", "label", "boxes"]),
]
)
processed_2 = transformer_1(processed_1)
affine = processed_1['image_meta_dict']['affine']
filename = scan_dict['image'].split(directory_split)[-1].split('.')[0]
filename_extension = '.' + '.'.join(scan_dict['image'].split(directory_split)[-1].split('.')[1:])
normalized_image = processed_2['image']
image_save = nibabel.Nifti1Image(normalized_image, affine)
boxes_save = nibabel.Nifti1Image(processed_2['boxes'], affine)
label_save = nibabel.Nifti1Image(processed_2['label'], affine)
nibabel.save(image_save, f"{save_directory}{directory_split}Images{directory_split}{filename}_{lung}{filename_extension}")
nibabel.save(boxes_save, f"{save_directory}{directory_split}Boxes{directory_split}{filename}_{lung}{filename_extension}")
nibabel.save(label_save, f"{save_directory}{directory_split}Labels{directory_split}{filename}_{lung}{filename_extension}")
passed = 0
def process_scan(scan_dict, save_directory):
global passed
try:
masked, affine = mask_lung(scan_dict, batch_size=5)
#s = nibabel.Nifti1Image(masked, affine)
#nibabel.save(s, "D:\\Datasets\\Temp\\Images\\test4.nii.gz")
extremes = calculate_extremes(np.copy(masked), 1)
process_lung_scan(scan_dict, save_directory, extremes, "right")
extremes = calculate_extremes(masked, 2)
process_lung_scan(scan_dict, save_directory, extremes, "left")
except:
passed += 1
logger.LogWarning("Skipped scan", [str(scan_dict)])
print(f"passed {passed}")
def process_directory(directory, save_directory):
for image_file in glob.glob(f"{directory}{directory_split}Images{directory_split}*.nii.gz"):
filename = image_file.split(directory_split)[-1]
scan_dict = {
'image' : image_file,
'label' : f"{directory}{directory_split}Labels{directory_split}{filename}",
'boxes' : f"{directory}{directory_split}Boxes{directory_split}{filename}"
}
print(f"Processing {filename}")
process_scan(scan_dict, save_directory)
if __name__ == "__main__":
load_folder = "/home/tumor/data/MSD/"
store_folder = "/home/tumor/data/MSD-Lung/"
#load_folder = "D:\\Datasets\\Temp\\"
#store_folder = "D:\\Datasets\\Temp\\Save\\"
logger.LogInfo("Started cropping lungs", [])
process_directory(load_folder, store_folder)
logger.LogMilestone("Finished cropping lungs!", [])
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