def creat_data(path_name, spacing_path, gap_size, save_num):
spacing_info = np.loadtxt(spacing_path, delimiter=",", dtype=np.float32)
proximity_list = []
patch_name = []
i = save_num
print("processing dataset %d" % i)
image_pre_fix = path_name + '0' + str(i) + '/' + 'image' + '0' + str(i)
file_name = image_pre_fix + '.nii.gz'
src_array = sitk.GetArrayFromImage(
sitk.ReadImage(file_name, sitk.sitkFloat32))
spacing_x = spacing_info[i][0]
spacing_y = spacing_info[i][1]
spacing_z = spacing_info[i][2]
re_spacing_img, curr_spacing, resize_factor = resample(
src_array, np.array([spacing_z, spacing_x, spacing_y]),
np.array([1, 1, 1]))
max_z, max_y, max_x = re_spacing_img.shape
print('new shape:', re_spacing_img.shape)
ostia_points = []
for j in range(4):
reference_path = '/data_process_tools/train_data/dataset0' + str(
i) + '/vessel' + str(j) + '/pointS.txt'
txt_data = np.loadtxt(reference_path, dtype=np.float32)
if j == 0 or j == 1:
print('0:', txt_data)
ostia_points.append(txt_data)
else:
ostia_points[1] = ostia_points[1] + txt_data
print(ostia_points[1])
ostia_points[1] = ostia_points[1] / 3
print('ostia points:', ostia_points)
min_range = 17
# max_range = 100
max_points = 100
counter = 0
record_set = set()
for op in ostia_points:
max_range = get_max_boundr([max_x, max_y, max_z], op)
for k in range(min_range, int(max_range + 1)):
x_list, y_list, z_list = get_shell(max_points, k)
record_set.add(
(int(round(op[0])), int(round(op[1])), int(round(op[2]))))
for m in range(len(x_list)):
new_x = int(round(op[0] + x_list[m]))
new_y = int(round(op[1] + y_list[m]))
new_z = int(round(op[2] + z_list[m]))
check_temp = (new_x, new_y, new_z)
if check_temp not in record_set:
record_set.add(check_temp)
center_x_pixel = new_x
center_y_pixel = new_y
center_z_pixel = new_z
target_point = np.array(
[center_x_pixel, center_y_pixel, center_z_pixel])
print("new center:", target_point)
min_dis = np.linalg.norm(target_point - op)
print('min dis:', min_dis)
curr_proximity = get_proximity(min_dis, cutoff_value=16)
print('proximity:', curr_proximity)
cut_size = 9
left_x = center_x_pixel - cut_size
right_x = center_x_pixel + cut_size
left_y = center_y_pixel - cut_size
right_y = center_y_pixel + cut_size
left_z = center_z_pixel - cut_size
right_z = center_z_pixel + cut_size
if (right_z + 1
) < len(re_spacing_img) and left_z >= 0 and (
right_y + 1) < max_y and left_y >= 0 and (
right_x + 1
) < max_x and left_x >= 0 and curr_proximity <= 0:
new_src_arr = np.zeros(
(cut_size * 2 + 1, cut_size * 2 + 1,
cut_size * 2 + 1))
for ind in range(left_z, right_z + 1):
src_temp = re_spacing_img[ind].copy()
new_src_arr[ind -
left_z] = src_temp[left_y:right_y + 1,
left_x:right_x + 1]
folder_path = './patch_data/ostia_patch/negative/' + 'gp_' + str(
gap_size) + '/d' + str(i)
if not os.path.exists(folder_path):
os.makedirs(folder_path)
record_name = 'ostia_patch/negative/' + 'gp_' + str(
gap_size) + '/d' + str(i) + '/' + 'd_' + str(
i) + '_' + 'x_' + str(
center_x_pixel) + '_y_' + str(
center_y_pixel) + '_z_' + str(
center_z_pixel) + '.nii.gz'
print(record_name)
org_name = './patch_data/' + record_name
out = sitk.GetImageFromArray(new_src_arr)
sitk.WriteImage(out, org_name)
proximity_list.append(curr_proximity)
patch_name.append(record_name)
counter += 1
else:
print('out of bounder skip this block')
return patch_name, proximity_list
def creat_data(path_name, spacing_path, save_num, cut_size=19, move_step=3):
spacing_info = np.loadtxt(spacing_path, delimiter=",", dtype=np.float32)
proximity_list = []
patch_name = []
i = save_num
print("processing dataset %d" % i)
image_pre_fix = path_name + '0' + str(i) + '/' + 'image' + '0' + str(i)
file_name = image_pre_fix + '.nii.gz'
src_array = sitk.GetArrayFromImage(
sitk.ReadImage(file_name, sitk.sitkFloat32))
spacing_x = spacing_info[i][0]
spacing_y = spacing_info[i][1]
spacing_z = spacing_info[i][2]
re_spacing_img, curr_spacing, resize_factor = resample(
src_array, np.array([spacing_z, spacing_x, spacing_y]),
np.array([1, 1, 1]))
vessels = []
for j in range(4):
reference_path = './train_data/dataset0' + str(i) + '/vessel' + str(
j) + '/reference.txt'
txt_data = np.loadtxt(reference_path, dtype=np.float32)
center = txt_data[..., 0:3]
vessels.append(center)
z, h, w = re_spacing_img.shape
for iz in range(int((z - cut_size) / move_step + 1)):
for ih in range(int((h - cut_size) / move_step + 1)):
for iw in range(int((w - cut_size) / move_step + 1)):
sz = iz * move_step
ez = iz * move_step + cut_size
sh = ih * move_step
eh = ih * move_step + cut_size
sw = iw * move_step
ew = iw * move_step + cut_size
center_z = (ez - sz) // 2 + sz
center_y = (eh - sh) // 2 + sh
center_x = (ew - sw) // 2 + sw
target_point = np.array([center_x, center_y, center_z])
print("new center:", target_point)
min_dis = get_closer_distence(vessels, target_point)
print('min dis:', min_dis)
curr_proximity = get_proximity(min_dis)
print('proximity:', curr_proximity)
if curr_proximity <= 0.0:
proximity_list.append(curr_proximity)
new_src_arr = np.zeros((cut_size, cut_size, cut_size))
for ind in range(sz, ez):
src_temp = re_spacing_img[ind].copy()
new_src_arr[ind - sz] = src_temp[sh:eh, sw:ew]
folder_path = './patch_data/seeds_patch/negative/' + 'gp_' + str(
move_step) + '/d' + str(i)
if not os.path.exists(folder_path):
os.makedirs(folder_path)
record_name = 'seeds_patch/negative/' + 'gp_' + str(
move_step) + '/d' + str(i) + '/' + 'd_' + str(
i) + '_' + 'x_' + str(center_x) + '_y_' + str(
center_y) + '_z_' + str(center_z) + '.nii.gz'
# print(record_name)
org_name = './patch_data/' + record_name
out = sitk.GetImageFromArray(new_src_arr)
sitk.WriteImage(out, org_name)
patch_name.append(record_name)
return patch_name, proximity_list
def creat_data(path_name, spacing_path, gap_size, save_num):
spacing_info = np.loadtxt(spacing_path, delimiter=",", dtype=np.float32)
proximity_list = []
patch_name = []
i = save_num
print("processing dataset %d" % i)
image_pre_fix = path_name + '0' + str(i) + '/' + 'image' + '0' + str(i)
file_name = image_pre_fix + '.nii.gz'
src_array = sitk.GetArrayFromImage(
sitk.ReadImage(file_name, sitk.sitkFloat32))
spacing_x = spacing_info[i][0]
spacing_y = spacing_info[i][1]
spacing_z = spacing_info[i][2]
re_spacing_img, curr_spacing, resize_factor = resample(
src_array, np.array([spacing_z, spacing_x, spacing_y]),
np.array([1, 1, 1]))
max_z, max_y, max_x = re_spacing_img.shape
vessels = []
for j in range(4):
reference_path = './train_data/dataset0' + str(i) + '/vessel' + str(
j) + '/reference.txt'
txt_data = np.loadtxt(reference_path, dtype=np.float32)
temp_center = txt_data[..., 0:3]
vessels.append(temp_center)
record_set = set()
max_range = 4
max_points = 30
for v in range(4):
print("processing vessel %d" % v)
reference_path = path_name + '0' + str(i) + '/' + 'vessel' + str(
v) + '/' + 'reference.txt'
txt_data = np.loadtxt(reference_path, dtype=np.float32)
center = txt_data[..., 0:3]
counter = 0
last_center_x_pixel = -1
last_center_y_pixel = -1
last_center_z_pixel = -1
for j in range(len(center)):
center_x = center[j][0]
center_y = center[j][1]
center_z = center[j][2]
record_set.add((center_x, center_y, center_z))
if j % gap_size == 0:
org_x_pixel = int(round(center_x))
org_y_pixel = int(round(center_y))
org_z_pixel = int(round(center_z))
record_set.add((org_x_pixel, org_y_pixel, org_z_pixel))
if org_x_pixel != last_center_x_pixel or org_y_pixel != last_center_y_pixel or org_z_pixel != last_center_z_pixel:
last_center_x_pixel = org_x_pixel
last_center_y_pixel = org_y_pixel
last_center_z_pixel = org_z_pixel
for k in range(1, max_range + 1):
x_list, y_list, z_list = get_shell(max_points, k)
for m in range(len(x_list)):
new_x = int(round(center_x + x_list[m]))
new_y = int(round(center_y + y_list[m]))
new_z = int(round(center_z + z_list[m]))
check_temp = (new_x, new_y, new_z)
if check_temp not in record_set:
record_set.add(check_temp)
center_x_pixel = new_x
center_y_pixel = new_y
center_z_pixel = new_z
target_point = np.array([
center_x_pixel, center_y_pixel,
center_z_pixel
])
print("new center:", target_point)
min_dis = get_closer_distence(
vessels, target_point)
curr_proximity = get_proximity(min_dis,
cutoff_value=4)
print('proximity:', curr_proximity)
cut_size = 9
left_x = center_x_pixel - cut_size
right_x = center_x_pixel + cut_size
left_y = center_y_pixel - cut_size
right_y = center_y_pixel + cut_size
left_z = center_z_pixel - cut_size
right_z = center_z_pixel + cut_size
if (
right_z + 1
) < len(re_spacing_img) and left_z >= 0 and (
right_y + 1
) < max_y and left_y >= 0 and (
right_x + 1
) < max_x and left_x >= 0 and curr_proximity > 0:
new_src_arr = np.zeros(
(cut_size * 2 + 1, cut_size * 2 + 1,
cut_size * 2 + 1))
for ind in range(left_z, right_z + 1):
src_temp = re_spacing_img[ind].copy()
new_src_arr[ind - left_z] = src_temp[
left_y:right_y + 1,
left_x:right_x + 1]
folder_path = './patch_data/seeds_patch/positive/' + 'gp_' + str(
gap_size) + '/d' + str(i)
if not os.path.exists(folder_path):
os.makedirs(folder_path)
record_name = 'seeds_patch/positive/' + 'gp_' + str(
gap_size
) + '/d' + str(i) + '/' + 'd_' + str(
i) + '_v_' + str(v) + '_x_' + str(
center_x_pixel) + '_y_' + str(
center_y_pixel) + '_z_' + str(
center_z_pixel) + '.nii.gz'
print(record_name)
org_name = './patch_data/' + record_name
out = sitk.GetImageFromArray(new_src_arr)
sitk.WriteImage(out, org_name)
proximity_list.append(curr_proximity)
patch_name.append(record_name)
counter += 1
else:
print('out of bounder skip this block')
# break
return patch_name, proximity_list