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import pydicom as dicomio
import SimpleITK as sitk
import glob
import xml.etree.ElementTree as ET
import platform
import numpy as np
import nibabel
directory_split = "\\" if platform.system() == "Windows" else "/"
def get_length(number_of_slices, thickness):
return (number_of_slices * thickness) / 10
def convert_to_nifti(dicom_folder, save_path):
image = []
voxel_spacing = None
origin = None
for dcm_f in glob.glob(dicom_folder + f"{directory_split}*.dcm"):
dicom = dicomio.read_file(dcm_f, force=True)
if(dicom.Modality != 'CT' or hasattr(dicom, 'SecondaryCaptureDeviceID')):
return False
layer = dicom.pixel_array
layer = layer.astype('float32')
layer *= dicom.RescaleSlope
layer += dicom.RescaleIntercept
origin = dicom.ImagePositionPatient
if(type(dicom) == dicomio.FileDataset):
voxel_spacing = [dicom.PixelSpacing[0], dicom.PixelSpacing[1], dicom.SliceThickness]
else:
voxel_spacing = [dicom.PixelSpacing[0], dicom.PixelSpacing[1], dicom.SpacingBetweenSlices]
image.append(layer)
affine = np.zeros((4,4))
affine[0][0] = voxel_spacing[0] * origin[0]/np.abs(origin[0])
affine[0][-1] = origin[0] * origin[0]/np.abs(origin[0])
affine[1][1] = voxel_spacing[1] * origin[1]/np.abs(origin[1])
affine[1][-1] = origin[1] * origin[1]/np.abs(origin[1])
affine[2][2] = voxel_spacing[2]
affine[2][-1] = origin[2]
affine[-1][-1] = 1
image.reverse()
nifti = nibabel.Nifti1Image(np.swapaxes(np.array(image),0,2), affine)
nifti.update_header()
if(get_length(len(image), voxel_spacing[-1]) > 16 and get_length(len(image), voxel_spacing[-1]) < 60):
nibabel.save(nifti, save_path)
return True
return False
def generate_segbox(annotations, dicom_folder, save_path):
image = []
keys = annotations.keys()
voxel_spacing = None
origin = None
for dcm_f in glob.glob(dicom_folder + f"{directory_split}*.dcm"):
dicom = dicomio.read_file(dcm_f, force=True)
origin = dicom.ImagePositionPatient
if(type(dicom) == dicomio.FileDataset):
voxel_spacing = [dicom.PixelSpacing[0], dicom.PixelSpacing[1], dicom.SliceThickness]
else:
voxel_spacing = [dicom.PixelSpacing[0], dicom.PixelSpacing[1], dicom.SpacingBetweenSlices]
uid = dicom.SOPInstanceUID
layer = np.zeros(dicom.pixel_array.shape)
if(uid in keys):
bndbox = annotations[uid]
layer[bndbox[1] - 1:bndbox[3], bndbox[0] - 1:bndbox[2]] = 1
image.append(layer)
affine = np.zeros((4,4))
affine[0][0] = voxel_spacing[0] * origin[0]/np.abs(origin[0])
affine[0][-1] = origin[0] * origin[0]/np.abs(origin[0])
affine[1][1] = voxel_spacing[1] * origin[1]/np.abs(origin[1])
affine[1][-1] = origin[1] * origin[1]/np.abs(origin[1])
affine[2][2] = voxel_spacing[2]
affine[2][-1] = origin[2]
affine[-1][-1] = 1
image.reverse()
nifti = nibabel.Nifti1Image(np.swapaxes(np.array(image),0,2), affine)
nifti.update_header()
if(get_length(len(image), voxel_spacing[-1]) > 20 and get_length(len(image), voxel_spacing[-1]) < 60):
nibabel.save(nifti, save_path)
def load_annotations(folder):
annotations = {}
for file in glob.glob(folder + f"{directory_split}*.xml"):
annotations[file.replace('.xml', '').split(directory_split)[-1]] = extract_boundingbox(file)
return annotations
def extract_boundingbox(file_path):
try:
tree = ET.parse(file_path)
root = tree.getroot()
bndbox_xml = tree.findall('*/bndbox')[0]
xmin = float(bndbox_xml.find('xmin').text)
ymin = float(bndbox_xml.find('ymin').text)
xmax = float(bndbox_xml.find('xmax').text)
ymax = float(bndbox_xml.find('ymax').text)
return (int(xmin), int(ymin), int(xmax), int(ymax))
except:
return (0, 0, 0, 0)
def convert_nsclc_petct(annotations_folder, nsclc_folder, label_folder, input_folder):
patient_folders = glob.glob(nsclc_folder + f"{directory_split}*")
for annotation_folder in glob.glob(annotations_folder + f"{directory_split}*"):
patient = annotation_folder.split(directory_split)[-1]
patient_folder = [p for p in patient_folders if patient in p][0]
annotations = load_annotations(annotation_folder)
scan_number = 1
for examination_folder in glob.glob(patient_folder + f"{directory_split}*"):
for scan_folder in glob.glob(examination_folder + f"{directory_split}*"):
print(f"Converting {scan_folder}")
if(convert_to_nifti(scan_folder, f"{input_folder}{directory_split}{patient}_scan{scan_number}.nii.gz")):
generate_segbox(annotations, scan_folder, f"{label_folder}{directory_split}{patient}_scan{scan_number}.nii.gz")
print(f"Saved {patient}_scan{scan_number}.nii.gz")
scan_number += 1
if __name__ == "__main__":
#pass
annotations = "D:\\Datasets\\NSCLC-Lung-PET-CT\\Temp\\Annotations\\"
nsclcs_folder = "D:\\Datasets\\NSCLC-Lung-PET-CT\\Temp\Patients\\"
labels = "D:\\Datasets\\NSCLC-Lung-PET-CT\\Temp\\Boxes\\"
inputs = "D:\\Datasets\\NSCLC-Lung-PET-CT\\Temp\\Images\\"
convert_nsclc_petct(annotations, nsclcs_folder, labels, inputs) |