def infer(start: list):
"""
:param start: Initial point
:return: Moving position, the index of maximum confidence direction, Current termination probability
"""
max_z = re_spacing_img.shape[0]
max_x = re_spacing_img.shape[1]
max_y = re_spacing_img.shape[2]
cut_size = 9
spacing_x = spacing[0]
spacing_y = spacing[1]
spacing_z = spacing[2]
center_x_pixel = get_spacing_res2(start[0], spacing_x, resize_factor[1])
center_y_pixel = get_spacing_res2(start[1], spacing_y, resize_factor[2])
center_z_pixel = get_spacing_res2(start[2], spacing_z, resize_factor[0])
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
new_patch = np.zeros(
(cut_size * 2 + 1, cut_size * 2 + 1, cut_size * 2 + 1))
if not (left_x < 0 or right_x < 0 or left_y < 0 or right_y < 0
or left_z < 0 or right_z < 0 or left_x >= max_x or right_x >= max_x
or left_y >= max_y or right_y >= max_y or left_z >= max_z
or right_z >= max_z):
for ind in range(left_z, right_z + 1):
src_temp = re_spacing_img[ind].copy()
new_patch[ind - left_z] = src_temp[left_y:right_y + 1,
left_x:right_x + 1]
input_data = data_preprocess(new_patch)
inputs = input_data.to(device)
outputs = infer_model(inputs.float())
outputs = outputs.view((len(input_data), max_points + 1))
outputs_1 = outputs[:, :len(outputs[0]) - 1]
outputs_2 = outputs[:, -1]
outputs_1 = torch.nn.functional.softmax(outputs_1, 1)
indexs = np.argsort(outputs_1.cpu().detach().numpy()[0])[::-1]
curr_prob = prob_terminates(outputs_1,
max_points).cpu().detach().numpy()[0]
curr_r = outputs_2.cpu().detach().numpy()[0]
sx, sy, sz = get_shell(max_points, curr_r)
return [sx, sy, sz], indexs, curr_r, curr_prob
else:
return None
def creat_data(max_points, pi_folder, spacing_path, gap_size):
'''
:param max_points:
:param pi_folder:
:param spacing_path:
:param gap_size:
:return:
'''
pre_ind_list = []
next_ind_list = []
radials_list = []
patch_name = []
print("processing dataset ", pi_folder)
pi = os.path.basename(pi_folder)
image_pre_fix = pi_folder
icafem = iCafe(pi_folder)
file_name = image_pre_fix + '/TH_' + pi + '.tif'
src_array = sitk.GetArrayFromImage(
sitk.ReadImage(file_name, sitk.sitkFloat32))
#re_spacing_img, curr_spacing, resize_factor = resample(src_array, np.array([spacing_z, spacing_x, spacing_y]),
# np.array([0.5, 0.5, 0.5]))
re_spacing_img = src_array
curr_mean = np.array([0, 0, 0])
rotate_prob = 0.3
#v is vessel id
for v in range(icafem.snakelist.NSnakes):
if icafem.snakelist[v].length < 10:
continue
print("processing vessel %d" % v)
center = np.array([
icafem.snakelist[v][j].pos.lst()
for j in range(icafem.snakelist[v].NP)
])
radials_data = np.array([
icafem.snakelist[v][j].rad for j in range(icafem.snakelist[v].NP)
])
start_ind = 0
end_ind = icafem.snakelist[v].NP
#print("start ind:", start_ind)
#print("end ind:", end_ind)
counter = 0
last_center_x_pixel = -1
last_center_y_pixel = -1
last_center_z_pixel = -1
# for j in range(start_ind, start_ind+1):
for j in range(start_ind, end_ind):
if j % gap_size == 0:
if j % 10 == 0:
print('\rj:', j, end='')
center_x = icafem.snakelist[v][j].pos.x
center_y = icafem.snakelist[v][j].pos.y
center_z = icafem.snakelist[v][j].pos.z
org_x_pixel = center_x
org_y_pixel = center_y
org_z_pixel = center_z
if org_x_pixel != last_center_x_pixel or org_y_pixel != last_center_y_pixel or org_z_pixel != last_center_z_pixel:
#print("last:",[last_center_x_pixel,last_center_y_pixel,last_center_z_pixel])
#print("curr:",[org_x_pixel, org_y_pixel, org_z_pixel])
last_center_x_pixel = org_x_pixel
last_center_y_pixel = org_y_pixel
last_center_z_pixel = org_z_pixel
radial = icafem.snakelist[v][j].rad
if radial == 0:
continue
record_set = set()
curr_conv = np.array([[radial * 0.25, 0.0, 0.0],
[0.0, radial * 0.25, 0.0],
[0.0, 0.0, radial * 0.25]])
# To then obtain an off-centerline sample, point x is translated using a random shift sampled from a 3D normal distribution with μ = 0.0, σ = 0.25r
for k in range(10):
off_center_x, off_center_y, off_center_z = np.random.multivariate_normal(
mean=curr_mean, cov=curr_conv, size=1).T
center_x_new = center_x + off_center_x[0]
center_y_new = center_y + off_center_y[0]
center_z_new = center_z + off_center_z[0]
center_x_pixel = center_x_new
center_y_pixel = center_y_new
center_z_pixel = center_z_new
while True:
if (center_x_pixel != org_x_pixel
or center_y_pixel != org_y_pixel
or center_z_pixel != org_z_pixel) and (
center_x_pixel, center_y_pixel,
center_z_pixel) not in record_set:
record_set.add((center_x_pixel, center_y_pixel,
center_z_pixel))
break
else:
off_center_x, off_center_y, off_center_z = np.random.multivariate_normal(
mean=curr_mean, cov=curr_conv, size=1).T
center_x_new = int(
round(center_x + off_center_x[0]))
center_y_new = int(
round(center_y + off_center_y[0]))
center_z_new = int(
round(center_z + off_center_z[0]))
center_x_pixel = center_x_new
center_y_pixel = center_y_new
center_z_pixel = center_z_new
new_radial_ind = get_new_radial_ind(
center, [center_x_new, center_y_new, center_z_new])
new_radial = radials_data[new_radial_ind]
sx, sy, sz = get_shell(max_points, new_radial)
shell_arr = np.zeros((len(sx), 3))
for s_ind in range(len(sx)):
shell_arr[s_ind][0] = sx[s_ind]
shell_arr[s_ind][1] = sy[s_ind]
shell_arr[s_ind][2] = sz[s_ind]
pre_ind, next_ind = get_pre_next_point_ind(
center, radials_data, new_radial_ind)
if pre_ind != -1 and next_ind != -1:
cut_size = 9
left_x = int(center_x_pixel - cut_size)
right_x = int(center_x_pixel + cut_size)
if left_x < 0 or right_x >= icafem.SM:
continue
left_y = int(center_y_pixel - cut_size)
right_y = int(center_y_pixel + cut_size)
if left_x < 0 or right_y >= icafem.SN:
continue
left_z = int(center_z_pixel - cut_size)
right_z = int(center_z_pixel + cut_size)
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):
if ind < 0 or ind >= icafem.SZ:
continue
src_temp = re_spacing_img[ind].copy()
if src_temp[left_y:right_y + 1,
left_x:right_x +
1].shape != (cut_size * 2 + 1,
cut_size * 2 + 1):
print('patch size mismatch')
continue
new_src_arr[ind -
left_z] = src_temp[left_y:right_y +
1,
left_x:right_x +
1]
if np.random.uniform() <= rotate_prob:
curr_c = [
center_x_new, center_y_new, center_z_new
]
new_src_arr, new_pre_cood, new_next_cood = rotate_augmentation(
new_src_arr,
pre_ind,
next_ind,
curr_c,
center,
angle_x=(-60. / 360 * 2. * np.pi,
60. / 360 * 2. * np.pi),
angle_y=(-60. / 360 * 2. * np.pi,
60. / 360 * 2. * np.pi),
angle_z=(-60. / 360 * 2. * np.pi,
60. / 360 * 2. * np.pi))
p = [
new_pre_cood[0], new_pre_cood[1],
new_pre_cood[2]
]
pre_sim = find_closer_point_angle(
shell_arr, p, curr_c)
p = [
new_next_cood[0], new_next_cood[1],
new_next_cood[2]
]
next_sim = find_closer_point_angle(
shell_arr, p, curr_c)
pre_ind_list.append(pre_sim)
next_ind_list.append(next_sim)
radials_list.append(new_radial)
else:
pre_x = center[pre_ind][0]
pre_y = center[pre_ind][1]
pre_z = center[pre_ind][2]
next_x = center[next_ind][0]
next_y = center[next_ind][1]
next_z = center[next_ind][2]
curr_c = [
center_x_new, center_y_new, center_z_new
]
p = [pre_x, pre_y, pre_z]
pre_sim = find_closer_point_angle(
shell_arr, p, curr_c)
p = [next_x, next_y, next_z]
next_sim = find_closer_point_angle(
shell_arr, p, curr_c)
pre_ind_list.append(pre_sim)
next_ind_list.append(next_sim)
radials_list.append(new_radial)
folder_path = r'D:\LiChen\LATTEpatch/careii_patch/offset/point_' + str(
max_points) + '_gp_' + str(
gap_size) + '/' + 'd' + pi
if not os.path.exists(folder_path):
os.makedirs(folder_path)
record_name = 'careii_patch/offset/point_' + str(
max_points
) + '_gp_' + str(
gap_size
) + '/' + 'd' + pi + '/' + 'd_' + pi + '_' + 'v_' + str(
v) + '_' + 'patch_%d_' % counter + str(
k) + '.nii.gz'
org_name = r'D:\LiChen\LATTEpatch/' + record_name
out = sitk.GetImageFromArray(new_src_arr)
sitk.WriteImage(out, org_name)
patch_name.append(record_name)
counter += 1
return pre_ind_list, next_ind_list, radials_list, patch_name
def creat_data(max_points, path_name, spacing_path, gap_size, save_num):
spacing_info = np.loadtxt(spacing_path, delimiter=",", dtype=np.float32)
pre_ind_list = []
next_ind_list = []
radials_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([0.5, 0.5, 0.5]))
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]
radials_data = txt_data[..., 3]
start_ind = get_start_ind(center, radials_data)
end_ind = get_end_ind(center, radials_data)
print("start ind:", start_ind)
print("end ind:", end_ind)
counter = 0
last_center_x_pixel = -1
last_center_y_pixel = -1
last_center_z_pixel = -1
for j in range(start_ind, end_ind + 1):
# for j in range(start_ind, start_ind + 1):
if j % gap_size == 0:
print('j:', j)
center_x = center[j][0]
center_y = center[j][1]
center_z = center[j][2]
org_x_pixel = get_spacing_res2(center_x, spacing_x,
resize_factor[1])
org_y_pixel = get_spacing_res2(center_y, spacing_y,
resize_factor[2])
org_z_pixel = get_spacing_res2(center_z, spacing_z,
resize_factor[0])
if org_x_pixel != last_center_x_pixel or org_y_pixel != last_center_y_pixel or org_z_pixel != last_center_z_pixel:
print("last:", [
last_center_x_pixel, last_center_y_pixel,
last_center_z_pixel
])
print("curr:", [org_x_pixel, org_y_pixel, org_z_pixel])
last_center_x_pixel = org_x_pixel
last_center_y_pixel = org_y_pixel
last_center_z_pixel = org_z_pixel
radial = radials_data[j]
pre_ind, next_ind = get_pre_next_point_ind(
center, radials_data, j)
if pre_ind != -1 and next_ind != -1:
pre_x = center[pre_ind][0]
pre_y = center[pre_ind][1]
pre_z = center[pre_ind][2]
next_x = center[next_ind][0]
next_y = center[next_ind][1]
next_z = center[next_ind][2]
sx, sy, sz = get_shell(max_points, radial)
shell_arr = np.zeros((len(sx), 3))
for s_ind in range(len(sx)):
shell_arr[s_ind][0] = sx[s_ind]
shell_arr[s_ind][1] = sy[s_ind]
shell_arr[s_ind][2] = sz[s_ind]
center_x_pixel = get_spacing_res2(
center_x, spacing_x, resize_factor[1])
center_y_pixel = get_spacing_res2(
center_y, spacing_y, resize_factor[2])
center_z_pixel = get_spacing_res2(
center_z, spacing_z, resize_factor[0])
curr_c = [center_x, center_y, center_z]
p = [pre_x, pre_y, pre_z]
pre_sim = find_closer_point_angle(shell_arr, p, curr_c)
p = [next_x, next_y, next_z]
next_sim = find_closer_point_angle(
shell_arr, p, curr_c)
pre_ind_list.append(pre_sim)
next_ind_list.append(next_sim)
radials_list.append(radial)
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
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/centerline_patch/no_offset/point_' + str(
max_points) + '_gp_' + str(
gap_size) + '/' + 'd' + str(i)
if not os.path.exists(folder_path):
os.makedirs(folder_path)
record_name = 'centerline_patch/no_offset/point_' + str(
max_points
) + '_gp_' + str(gap_size) + '/' + 'd' + str(
i) + '/' + 'd_' + str(i) + '_' + 'v_' + str(
v) + '_' + 'patch_%d' % counter + '.nii.gz'
org_name = './patch_data/' + record_name
out = sitk.GetImageFromArray(new_src_arr)
sitk.WriteImage(out, org_name)
patch_name.append(record_name)
counter += 1
return pre_ind_list, next_ind_list, radials_list, patch_name
def creat_data(max_points, path_name, spacing_path, gap_size, save_num):
'''
:param max_points:
:param path_name:
:param spacing_path:
:param gap_size:
:param save_num:
:return:
'''
spacing_info = np.loadtxt(spacing_path, delimiter=",", dtype=np.float32)
pre_ind_list = []
next_ind_list = []
radials_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([0.5, 0.5, 0.5]))
curr_mean = np.array([0, 0, 0])
rotate_prob = 0.3
for v in range(4):
# for v in range(1):
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]
radials_data = txt_data[..., 3]
start_ind = get_start_ind(center, radials_data)
end_ind = get_end_ind(center, radials_data)
print("start ind:", start_ind)
print("end ind:", end_ind)
counter = 0
last_center_x_pixel = -1
last_center_y_pixel = -1
last_center_z_pixel = -1
# for j in range(start_ind, start_ind+1):
for j in range(start_ind, end_ind + 1):
if j % gap_size == 0:
print('j:', j)
center_x = center[j][0]
center_y = center[j][1]
center_z = center[j][2]
org_x_pixel = get_spacing_res2(center_x, spacing_x,
resize_factor[1])
org_y_pixel = get_spacing_res2(center_y, spacing_y,
resize_factor[2])
org_z_pixel = get_spacing_res2(center_z, spacing_z,
resize_factor[0])
if org_x_pixel != last_center_x_pixel or org_y_pixel != last_center_y_pixel or org_z_pixel != last_center_z_pixel:
print("last:", [
last_center_x_pixel, last_center_y_pixel,
last_center_z_pixel
])
print("curr:", [org_x_pixel, org_y_pixel, org_z_pixel])
last_center_x_pixel = org_x_pixel
last_center_y_pixel = org_y_pixel
last_center_z_pixel = org_z_pixel
radial = radials_data[j]
record_set = set()
curr_conv = np.array([[radial * 0.25, 0.0, 0.0],
[0.0, radial * 0.25, 0.0],
[0.0, 0.0, radial * 0.25]])
# To then obtain an off-centerline sample, point x is translated using a random shift sampled from a 3D normal distribution with μ = 0.0, σ = 0.25r
for k in range(10):
off_center_x, off_center_y, off_center_z = np.random.multivariate_normal(
mean=curr_mean, cov=curr_conv, size=1).T
center_x_new = center_x + off_center_x[0]
center_y_new = center_y + off_center_y[0]
center_z_new = center_z + off_center_z[0]
center_x_pixel = get_spacing_res2(
center_x_new, spacing_x, resize_factor[1])
center_y_pixel = get_spacing_res2(
center_y_new, spacing_y, resize_factor[2])
center_z_pixel = get_spacing_res2(
center_z_new, spacing_z, resize_factor[0])
while True:
if (center_x_pixel != org_x_pixel
or center_y_pixel != org_y_pixel
or center_z_pixel != org_z_pixel) and (
center_x_pixel, center_y_pixel,
center_z_pixel) not in record_set:
record_set.add((center_x_pixel, center_y_pixel,
center_z_pixel))
break
else:
off_center_x, off_center_y, off_center_z = np.random.multivariate_normal(
mean=curr_mean, cov=curr_conv, size=1).T
center_x_new = center_x + off_center_x[0]
center_y_new = center_y + off_center_y[0]
center_z_new = center_z + off_center_z[0]
center_x_pixel = get_spacing_res2(
center_x_new, spacing_x, resize_factor[1])
center_y_pixel = get_spacing_res2(
center_y_new, spacing_y, resize_factor[2])
center_z_pixel = get_spacing_res2(
center_z_new, spacing_z, resize_factor[0])
new_radial_ind = get_new_radial_ind(
center, [center_x_new, center_y_new, center_z_new])
new_radial = radials_data[new_radial_ind]
sx, sy, sz = get_shell(max_points, new_radial)
shell_arr = np.zeros((len(sx), 3))
for s_ind in range(len(sx)):
shell_arr[s_ind][0] = sx[s_ind]
shell_arr[s_ind][1] = sy[s_ind]
shell_arr[s_ind][2] = sz[s_ind]
pre_ind, next_ind = get_pre_next_point_ind(
center, radials_data, new_radial_ind)
# 只有找到了前一个点和后一个点才进入切割流程
if pre_ind != -1 and next_ind != -1:
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
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]
if np.random.uniform() <= rotate_prob:
curr_c = [
center_x_new, center_y_new, center_z_new
]
new_src_arr, new_pre_cood, new_next_cood = rotate_augmentation(
new_src_arr,
pre_ind,
next_ind,
curr_c,
center,
angle_x=(-60. / 360 * 2. * np.pi,
60. / 360 * 2. * np.pi),
angle_y=(-60. / 360 * 2. * np.pi,
60. / 360 * 2. * np.pi),
angle_z=(-60. / 360 * 2. * np.pi,
60. / 360 * 2. * np.pi))
p = [
new_pre_cood[0], new_pre_cood[1],
new_pre_cood[2]
]
pre_sim = find_closer_point_angle(
shell_arr, p, curr_c)
p = [
new_next_cood[0], new_next_cood[1],
new_next_cood[2]
]
next_sim = find_closer_point_angle(
shell_arr, p, curr_c)
pre_ind_list.append(pre_sim)
next_ind_list.append(next_sim)
radials_list.append(new_radial)
else:
pre_x = center[pre_ind][0]
pre_y = center[pre_ind][1]
pre_z = center[pre_ind][2]
next_x = center[next_ind][0]
next_y = center[next_ind][1]
next_z = center[next_ind][2]
curr_c = [
center_x_new, center_y_new, center_z_new
]
p = [pre_x, pre_y, pre_z]
pre_sim = find_closer_point_angle(
shell_arr, p, curr_c)
p = [next_x, next_y, next_z]
next_sim = find_closer_point_angle(
shell_arr, p, curr_c)
pre_ind_list.append(pre_sim)
next_ind_list.append(next_sim)
radials_list.append(new_radial)
folder_path = './patch_data/centerline_patch/offset/point_' + str(
max_points) + '_gp_' + str(
gap_size) + '/' + 'd' + str(i)
if not os.path.exists(folder_path):
os.makedirs(folder_path)
record_name = 'centerline_patch/offset/point_' + str(
max_points
) + '_gp_' + str(gap_size) + '/' + 'd' + str(
i) + '/' + 'd_' + str(i) + '_' + 'v_' + str(
v) + '_' + 'patch_%d_' % counter + str(
k) + '.nii.gz'
org_name = './patch_data/' + record_name
out = sitk.GetImageFromArray(new_src_arr)
sitk.WriteImage(out, org_name)
patch_name.append(record_name)
counter += 1
return pre_ind_list, next_ind_list, radials_list, patch_name
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, 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
