def create_RT_On_Primary(path):
Dicom_reader = Dicom_to_Imagestack(get_images_mask=True)
for MRN in os.listdir(path):
MRN_path = os.path.join(path, MRN)
data_dict = {'Path': [], 'Images': [], 'Vals': []}
exams = []
for _, exams, _ in os.walk(MRN_path):
break
for exam in exams:
exam_path = os.path.join(MRN_path, exam)
Dicom_reader.make_array(exam_path)
Images = Dicom_reader.ArrayDicom
non_min = np.where(Images > np.min(Images))
if non_min:
data_dict['Images'].append(Images)
data_dict['Vals'].append(len(non_min[0]))
data_dict['Path'].append(exam_path)
primary = data_dict['Vals'].index(np.max(data_dict['Vals']))
out_RT = np.zeros(data_dict['Images'][primary].shape)
out_RT = np.expand_dims(out_RT, axis=-1)
out_RT = np.repeat(out_RT, repeats=len(data_dict['Vals']), axis=-1)
cast_index = 1
for index in range(len(data_dict['Vals'])):
if index == primary:
continue
Images = data_dict['Images'][index]
output = np.zeros(Images.shape)
output[Images > np.min(Images)] = 1
out_RT[..., cast_index] = output
def print_rois_curate(base_path, check_rois=None):
associations = return_association_dict()
k = Dicom_to_Imagestack(get_images_mask=False,
associations=associations,
Contour_Names=check_rois)
k.down_folder(base_path)
for roi in k.all_rois:
print(roi)
print('Make an associations file form these roi lists')
def __init__(self):
self.Fill_Missing_Segments_Class = Fill_Missing_Segments()
self.Contour_Names = ['Liver'] + [
'Liver_Segment_{}'.format(i) for i in range(1, 9)
]
self.Out_Contour_names = [
'Liver_Segment_fixed_{}'.format(i) for i in range(1, 9)
]
self.Dicom_Reader = Dicom_to_Imagestack(
arg_max=False,
Contour_Names=self.Contour_Names,
delete_previous_rois=True)
class Niftii_To_Annotation(object):
def __init__(self, ROI_Names=None):
self.ROI_Names = ROI_Names
self.Dicom_reader = Dicom_to_Imagestack()
def niftii_to_annotation(self, dicom_path, nifti_annotation_path,
out_path):
self.Dicom_reader.make_array(dicom_path)
annotations = sitk.ReadImage(nifti_annotation_path)
annotations = sitk.GetArrayFromImage(annotations)
annotations = to_categorical(annotations)
# annotations[..., 1] += annotations[..., 2]
self.Dicom_reader.with_annotations(annotations,
out_path,
ROI_Names=self.ROI_Names)
def convert_rts(base_path):
for path, directories, files in os.walk(
base_path): # Look through all folders
files = [i for i in files
if i.endswith('.dcm')] # Stop if we have something with dicom
if files and len(
files
) > 5 and 'New_RT' not in directories: # Only go forward if we have at least 5 images
base_reader = Dicom_to_Imagestack(
get_images_mask=False
) # Make a base reader to identify the contour names
base_reader.down_folder(path)
contour_names = base_reader.rois_in_case # Keep the same nomenclature for naming
if not contour_names:
print('No contours found at {}'.format(path))
continue
reader = Dicom_to_Imagestack(
get_images_mask=True,
Contour_Names=contour_names,
arg_max=False
) # Now, create a new reader to create a mask of the contours
reader.Make_Contour_From_directory(
path) # Create the images and mask from the path
reader.use_template(
) # Shift over to our template for re-writing them
reader.with_annotations(
reader.mask,
output_dir=os.path.join(path, 'New_RT'),
ROI_Names=contour_names) # Write the new RT structure out
class Fix_Missing_Segments_Class(object):
def __init__(self):
self.Fill_Missing_Segments_Class = Fill_Missing_Segments()
self.Contour_Names = ['Liver'] + [
'Liver_Segment_{}'.format(i) for i in range(1, 9)
]
self.Out_Contour_names = [
'Liver_Segment_fixed_{}'.format(i) for i in range(1, 9)
]
self.Dicom_Reader = Dicom_to_Imagestack(
arg_max=False,
Contour_Names=self.Contour_Names,
delete_previous_rois=True)
def run_on_path(self, path):
self.path = path
self.Dicom_Reader.Make_Contour_From_directory(self.path)
def write_output_as_RT(self, annotations=None):
if annotations is None:
annotations = self.final_annotations
self.Dicom_Reader.with_annotations(annotations,
os.path.join(self.path, 'new_RS'),
ROI_Names=self.Out_Contour_names)
def iterate_mask(self):
try:
ground_truth = self.Dicom_Reader.mask[..., 1]
except:
return None
annotations = self.Dicom_Reader.mask[..., (0, 2, 3, 4, 5, 6, 7, 8, 9)]
overlap = np.sum(annotations, axis=-1)
annotations[overlap > 1] = 0
self.final_annotations = self.Fill_Missing_Segments_Class.iterate_annotations(
annotations,
ground_truth,
spacing=self.Dicom_Reader.annotation_handle.GetSpacing())
# annotations[ground_truth==0] = 0
# spacing = self.Dicom_Reader.annotation_handle.GetSpacing()
# re_organized_spacing = spacing[-1::-1]
# differences = [0,np.inf]
# index = 0
# while np.abs(differences[-1] - differences[-2]) > 50 and index < 15:
# index += 1
# print('Iterating {}'.format(index))
# previous_iteration = copy.deepcopy(np.argmax(annotations,axis=-1))
# annotations = remove_56_78(annotations)
# for i in range(1, annotations.shape[-1]):
# annotations[..., i] = remove_non_liver(annotations[..., i], do_3D=False, do_2D=True,min_area=10,spacing=re_organized_spacing)
# spacing = self.Dicom_Reader.annotation_handle.GetSpacing()
# annotations = self.Fill_Missing_Segments_Class.make_distance_map(annotations, ground_truth,spacing=spacing)
# differences.append(np.abs(np.sum(previous_iteration[ground_truth==1]-np.argmax(annotations,axis=-1)[ground_truth==1])))
# self.final_annotations = annotations
def write_output_as_nifti(self, out_path, images_desc, iteration):
new_annotations = sitk.GetImageFromArray(
np.argmax(self.final_annotations, axis=-1).astype('int8'))
new_annotations.SetSpacing(self.Dicom_Reader.dicom_handle.GetSpacing())
new_annotations.SetOrigin(self.Dicom_Reader.dicom_handle.GetOrigin())
new_annotations.SetDirection(
self.Dicom_Reader.dicom_handle.GetDirection())
if not os.path.exists(out_path):
os.makedirs(out_path)
image_path = os.path.join(
out_path,
'Overall_Data_' + images_desc + '_' + str(iteration) + '.nii.gz')
sitk.WriteImage(self.Dicom_Reader.dicom_handle, image_path)
annotation_path = os.path.join(
out_path,
'Overall_mask_' + images_desc + '_y' + str(iteration) + '.nii.gz')
sitk.WriteImage(new_annotations, annotation_path)
excel_file = os.path.join('..', 'Data',
'Post_treatment_and_Recurrence_info.xlsx')
output_file = os.path.join('..', 'Data',
'Post_treatment_and_Recurrence_info_output.xlsx')
status_path = os.path.join('..', 'Data', 'Status')
if not os.path.exists(status_path):
os.makedirs(status_path)
data = pd.read_excel(excel_file)
MRNs = [data['MRN'].array[-1]]
for MRN in MRNs:
index = list(data['MRN'].array).index(MRN)
MRN = str(data['MRN'][index])
print(MRN)
Recurrence = data['Recurrence'][index]
recurrence_path = os.path.join(images_path, MRN, Recurrence)
recurrence_reader = Dicom_to_Imagestack(
arg_max=False, Contour_Names=['Test_Ablation', 'Test_Cross'])
recurrence_reader.Make_Contour_From_directory(recurrence_path)
mask = recurrence_reader.mask
ablation_base = mask[..., 1]
cross_base = mask[..., 2]
centroid_of_ablation_recurrence = np.asarray(center_of_mass(ablation_base))
spacing = recurrence_reader.annotation_handle.GetSpacing()
output = create_output_ray(centroid_of_ablation_recurrence,
spacing=spacing,
ref_binary_image=cross_base,
margin_rad=np.deg2rad(2),
margin=50,
min_max=False)
recurrence_reader.with_annotations(output,
def __init__(self, ROI_Names=None):
self.ROI_Names = ROI_Names
self.Dicom_reader = Dicom_to_Imagestack()
out_file = os.path.join(patient_data_path,
desc + '_Iteration_' + str(iteration) + '.txt')
if not os.path.exists(out_file):
Dicom_Reader.Make_Contour_From_directory(patient_data_path)
Dicom_Reader.set_iteration(iteration)
Dicom_Reader.write_images_annotations(out_path)
iteration += 1
return None
associations = {
'Liver_BMA_Program_4': 'Liver',
'bma_liver': 'Liver',
'best_liver': 'Liver',
'tried_liver': 'Liver'
}
data_path = os.path.join('..', 'Data', 'Whole_Patients')
output_path = os.path.join('..', 'Data', 'Niftii_Arrays')
Dicom_Reader = Dicom_to_Imagestack(get_images_mask=False)
# Dicom_Reader.down_folder(data_path)
# all_rois = Dicom_Reader.all_rois
Dicom_Reader.set_associations(associations)
Dicom_Reader.set_get_images_and_mask(True)
Dicom_Reader.set_contour_names(['Liver'])
Dicom_Reader.Make_Contour_From_directory(
os.path.join(data_path, 'ABD_LYMPH_036'))
xxx = 1
# write_data(data_path,output_path, Dicom_Reader)
# Separate_files(output_path)
run_main(output_path, extension=5)