def removekidneysmallobj():
"""
去除Vnet肾脏结果的小物体
:return:
"""
kits_path = "D:\Data\kits19\kits19test\kidney_modify"
result_path = "D:\Data\kits19\kits19test\\kidney_modify\\"
"""
load itk image,change z Spacing value to 1,and save image ,liver mask ,tumor mask
:return:None
"""
# step2 get all train image
path_list = file_name_path(kits_path)
# step3 get signal train image and mask
for subsetindex in range(len(path_list)):
kits_subset_path = kits_path + "/" + str(path_list[subsetindex]) + "/"
images = []
index = 0
for _ in os.listdir(kits_subset_path):
image = cv2.imread(kits_subset_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
images.append(image)
index += 1
ys_pd_array = np.array(images)
ys_pd_array = np.reshape(ys_pd_array, (index, 512, 512))
ys_pd_sitk = sitk.GetImageFromArray(ys_pd_array)
ys_pd_array = removesmallConnectedCompont(ys_pd_sitk, 0.2)
ys_pd_array = np.clip(ys_pd_array, 0, 255).astype('uint8')
sub_src_path = result_path + path_list[subsetindex]
if not os.path.exists(sub_src_path):
os.makedirs(sub_src_path)
for i in range(np.shape(ys_pd_array)[0]):
cv2.imwrite(sub_src_path + "/" + str(i) + ".bmp", ys_pd_array[i])
def remove2d3dtumorsmallobj():
"""
去除2d和3dVnet的肿瘤的小物体
:return:
"""
tumor3d_path = "D:\Data\kits19\kits19test\\tumor3d"
tumor2d_path = "D:\Data\kits19\kits19test\\tumor2d"
"""
load itk image,change z Spacing value to 1,and save image ,liver mask ,tumor mask
:return:None
"""
# step2 get all train image
path_list = file_name_path(tumor3d_path)
# step3 get signal train image and mask
for subsetindex in range(len(path_list)):
kits_subset_path = tumor3d_path + "/" + str(path_list[subsetindex]) + "/"
tumor2d_subset_path = tumor2d_path + "/" + str(path_list[subsetindex]) + "/"
images = []
tumor2dmasks = []
index = 0
for _ in os.listdir(kits_subset_path):
image = cv2.imread(kits_subset_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
mask = cv2.imread(tumor2d_subset_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
images.append(image)
tumor2dmasks.append(mask)
index += 1
tumor3d_array = np.array(images)
tumor3d_array = np.reshape(tumor3d_array, (index, 512, 512))
tumor2d_array = np.array(tumor2dmasks)
tumor2d_array = np.reshape(tumor2d_array, (index, 512, 512))
tumor2d_array_sitk = sitk.GetImageFromArray(tumor2d_array)
tumor2d_array = removesmallConnectedCompont(tumor2d_array_sitk, 0.2)
tumor2d_array_sitk = sitk.GetImageFromArray(tumor3d_array)
tumor3d_array = removesmallConnectedCompont(tumor2d_array_sitk, 0.2)
tumor2d_array = np.clip(tumor2d_array, 0, 255).astype('uint8')
tumor3d_array = np.clip(tumor3d_array, 0, 255).astype('uint8')
sub_tumor3d_path = tumor3d_path + "/" + path_list[subsetindex]
sub_tumor2d_path = tumor2d_path + "/" + path_list[subsetindex]
if not os.path.exists(sub_tumor3d_path):
os.makedirs(sub_tumor3d_path)
if not os.path.exists(sub_tumor2d_path):
os.makedirs(sub_tumor2d_path)
for i in range(np.shape(tumor3d_array)[0]):
cv2.imwrite(sub_tumor3d_path + "/" + str(i) + ".bmp", tumor3d_array[i])
cv2.imwrite(sub_tumor2d_path + "/" + str(i) + ".bmp", tumor2d_array[i])
def tumor2d3dinkidney():
"""
保留肾脏区域范围内的肿瘤2d和3d结果
:return:
"""
tumor3d_path = "D:\Data\kits19\kits19test\\tumor3d"
tumor2d_path = "D:\Data\kits19\kits19test\\tumor2d"
kidney_path = "D:\Data\kits19\kits19test\kidney_modify"
result_path = "D:\Data\kits19\kits19test\\tumor_modify1\\"
"""
load itk image,change z Spacing value to 1,and save image ,liver mask ,tumor mask
:return:None
"""
# step2 get all train image
path_list = file_name_path(tumor3d_path)
# step3 get signal train image and mask
for subsetindex in range(len(path_list)):
kidney_subset_path = kidney_path + "/" + str(path_list[subsetindex]) + "/"
kits_subset_path = tumor3d_path + "/" + str(path_list[subsetindex]) + "/"
tumor2d_subset_path = tumor2d_path + "/" + str(path_list[subsetindex]) + "/"
kidneys = []
images = []
tumor2dmasks = []
index = 0
for _ in os.listdir(kits_subset_path):
kidney = cv2.imread(kidney_subset_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
image = cv2.imread(kits_subset_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
mask = cv2.imread(tumor2d_subset_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
kidneys.append(kidney)
images.append(image)
tumor2dmasks.append(mask)
index += 1
kidneys_array = np.array(kidneys)
kidneys_array = np.reshape(kidneys_array, (index, 512, 512))
kidneys_array_sitk = sitk.GetImageFromArray(kidneys_array)
kidneys_array = morphologicaloperation(kidneys_array_sitk, 5, "dilate")
tumor3d_array = np.array(images)
tumor3d_array = np.reshape(tumor3d_array, (index, 512, 512))
tumor2d_array = np.array(tumor2dmasks)
tumor2d_array = np.reshape(tumor2d_array, (index, 512, 512))
tumor3d_array = tumor3d_array & kidneys_array
tumor2d_array = tumor2d_array & kidneys_array
tumor_array = tumor3d_array | tumor2d_array
tumor_array_sitk = sitk.GetImageFromArray(tumor_array)
tumor_array = removesmallConnectedCompont(tumor_array_sitk, 0.1)
tumor_array = np.clip(tumor_array, 0, 255).astype('uint8')
sub_tumor_path = result_path + path_list[subsetindex]
if not os.path.exists(sub_tumor_path):
os.makedirs(sub_tumor_path)
for i in range(np.shape(tumor_array)[0]):
cv2.imwrite(sub_tumor_path + "/" + str(i) + ".bmp", tumor_array[i])
def inference():
depth_z = 48
Vnet3d = Vnet3dModule(512, 512, depth_z, channels=1, costname=("dice coefficient",), inference=True,
model_path="log\segmeation\model\Vnet3d.pd-20000")
test_path = "E:\junqiangchen\data\kits19\kits19process\\"
image_path = "Image"
mask_path = "Mask"
dice_values = []
for num in range(200, 210, 1):
index = 0
batch_xs = []
batch_ys = []
test_image_path = test_path + image_path + "/" + str(num)
test_mask_path = test_path + mask_path + "/" + str(num)
for _ in os.listdir(test_image_path):
image = cv2.imread(test_image_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
label = cv2.imread(test_mask_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
batch_xs.append(image)
batch_ys.append(label)
index += 1
xs_array = np.array(batch_xs)
ys_array = np.array(batch_ys)
xs_array = np.reshape(xs_array, (index, 512, 512))
ys_array = np.reshape(ys_array, (index, 512, 512))
ys_pd_array = np.empty((index, 512, 512), np.uint8)
last_depth = 0
for depth in range(0, index // depth_z, 1):
patch_xs = xs_array[depth * depth_z:(depth + 1) * depth_z, :, :]
pathc_pd = Vnet3d.prediction(patch_xs)
ys_pd_array[depth * depth_z:(depth + 1) * depth_z, :, :] = pathc_pd
last_depth = depth
if index != depth_z * last_depth:
patch_xs = xs_array[(index - depth_z):index, :, :]
pathc_pd = Vnet3d.prediction(patch_xs)
ys_pd_array[(index - depth_z):index, :, :] = pathc_pd
ys_pd_sitk = sitk.GetImageFromArray(ys_pd_array)
ys_pd_array = removesmallConnectedCompont(ys_pd_sitk, 0.4)
ys_pd_array = np.clip(ys_pd_array, 0, 255).astype('uint8')
dice_value = calcu_dice(ys_pd_array, ys_array)
print("num,dice:", (num, dice_value))
dice_values.append(dice_value)
for depth in range(0, index, 1):
cv2.imwrite(test_mask_path + "/" + str(depth) + "predict.bmp", ys_pd_array[depth])
average = sum(dice_values) / len(dice_values)
print("average dice:", average)
def tumor2d3dmerge():
"""
求2d和3d交集的区域分别与2d和3d相连接的区域都保留下来
:return:
"""
tumor3d_path = "D:\Data\kits19\kits19test\\tumor3d"
tumor2d_path = "D:\Data\kits19\kits19test\\tumor2d"
tumor2d3d_path = "D:\Data\kits19\kits19test\\tumor2_3d"
result_path = "D:\Data\kits19\kits19test\\tumor_modify2\\"
"""
load itk image,change z Spacing value to 1,and save image ,liver mask ,tumor mask
:return:None
"""
# step2 get all train image
path_list = file_name_path(tumor3d_path)
# step3 get signal train image and mask
for subsetindex in range(len(path_list)):
kits_subset_path = tumor3d_path + "/" + str(path_list[subsetindex]) + "/"
tumor2d_subset_path = tumor2d_path + "/" + str(path_list[subsetindex]) + "/"
tumor2d3d_subset_path = tumor2d3d_path + "/" + str(path_list[subsetindex]) + "/"
images = []
tumor2dmasks = []
tumo2d3dmasks = []
index = 0
for _ in os.listdir(kits_subset_path):
image = cv2.imread(kits_subset_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
mask = cv2.imread(tumor2d_subset_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
tumor = cv2.imread(tumor2d3d_subset_path + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE)
images.append(image)
tumor2dmasks.append(mask)
tumo2d3dmasks.append(tumor)
index += 1
tumor3d_array = np.array(images)
tumor3d_array = np.reshape(tumor3d_array, (index, 512, 512))
tumor2d_array = np.array(tumor2dmasks)
tumor2d_array = np.reshape(tumor2d_array, (index, 512, 512))
tumor2d3d_array = np.array(tumo2d3dmasks)
tumor2d3d_array = np.reshape(tumor2d3d_array, (index, 512, 512))
tumor_array = np.zeros((index, 512, 512), np.int)
for z in range(index):
tumor2d3d = tumor2d3d_array[z]
if np.max(tumor2d3d) != 0:
tumor2d3dlabels, tumor2d3dout = cv2.connectedComponents(tumor2d3d)
tumor3d = tumor3d_array[z]
tumor3dlabels, tumor3dout = cv2.connectedComponents(tumor3d)
tumor2d = tumor2d_array[z]
tumor2dlabels, tumor2dout = cv2.connectedComponents(tumor2d)
for i in range(1, tumor2d3dlabels):
tumor2d3doutmask = np.zeros(tumor2d3dout.shape, np.int)
tumor2d3doutmask[tumor2d3dout == i] = 255
for j in range(1, tumor3dlabels):
tumor3doutmask = np.zeros(tumor3dout.shape, np.int)
tumor3doutmask[tumor3dout == j] = 255
if cv2.countNonZero(tumor2d3doutmask & tumor3doutmask):
tumor_array[z] = tumor_array[z] + tumor3doutmask
for k in range(1, tumor2dlabels):
tumor2doutmask = np.zeros(tumor2dout.shape, np.int)
tumor2doutmask[tumor2dout == k] = 255
if cv2.countNonZero(tumor2d3doutmask & tumor2doutmask):
tumor_array[z] = tumor_array[z] + tumor2doutmask
tumor2d_array_sitk = sitk.GetImageFromArray(tumor_array)
tumor2d_array = removesmallConnectedCompont(tumor2d_array_sitk, 0.2)
tumor_array = np.clip(tumor2d_array, 0, 255).astype('uint8')
sub_tumor_path = result_path + path_list[subsetindex]
if not os.path.exists(sub_tumor_path):
os.makedirs(sub_tumor_path)
for i in range(np.shape(tumor_array)[0]):
cv2.imwrite(sub_tumor_path + "/" + str(i) + ".bmp", tumor_array[i])
def inference():
"""
Vnet network segmentation kidney fine segmatation
:return:
"""
depth_z = 32
Vnet3d = Vnet3dModule(512,
512,
depth_z,
channels=1,
costname=("dice coefficient", ),
inference=True,
model_path="log\segmeation\VNet\model\Vnet3d.pd")
kits_path = "D:\Data\kits19\kits19\\test"
image_name = "imaging.nii.gz"
result_path = "D:\Data\kits19\kits19test"
"""
load itk image,change z Spacing value to 1,and save image ,liver mask ,tumor mask
:return:None
"""
# step2 get all train image
path_list = file_name_path(kits_path)
read = open("kidneyrang.txt", 'r')
# step3 get signal train image and mask
for subsetindex in range(len(path_list)):
line = read.readline()
line = line.split(',')
casename = line[0]
start = int(line[1])
end = int(line[2][0:-1])
kits_subset_path = kits_path + "/" + str(path_list[subsetindex]) + "/"
file_image = kits_subset_path + image_name
# 1 load itk image and truncate value with upper and lower and get rang kideny region
src = load_itkfilewithtrucation(file_image, 300, -200)
originSpacing = src.GetSpacing()
src_array = sitk.GetArrayFromImage(src)
sub_src_array = src_array[:, :, start:end]
sub_src = sitk.GetImageFromArray(sub_src_array)
sub_src.SetSpacing(originSpacing)
print(sub_src.GetSize())
thickspacing, widthspacing = originSpacing[0], originSpacing[1]
# 2 change z spacing >1.0 to 1.0
if thickspacing > 1.0:
_, sub_src = resize_image_itk(
sub_src,
newSpacing=(1.0, widthspacing, widthspacing),
originSpcaing=(thickspacing, widthspacing, widthspacing),
resamplemethod=sitk.sitkLinear)
xs_array = sitk.GetArrayFromImage(sub_src)
xs_array = np.swapaxes(xs_array, 0, 2)
index = np.shape(xs_array)[0]
ys_pd_array = np.zeros(np.shape(xs_array), np.uint8)
last_depth = 0
for depth in range(0, index // depth_z, 1):
patch_xs = xs_array[depth * depth_z:(depth + 1) * depth_z, :, :]
pathc_pd = Vnet3d.prediction(patch_xs)
ys_pd_array[depth * depth_z:(depth + 1) * depth_z, :, :] = pathc_pd
last_depth = depth
if index != depth_z * last_depth:
patch_xs = xs_array[(index - depth_z):index, :, :]
pathc_pd = Vnet3d.prediction(patch_xs)
ys_pd_array[(index - depth_z):index, :, :] = pathc_pd
ys_pd_sitk = sitk.GetImageFromArray(ys_pd_array)
ys_pd_array = removesmallConnectedCompont(ys_pd_sitk, 0.2)
ys_pd_array = np.clip(ys_pd_array, 0, 255).astype('uint8')
sub_src_path = result_path + "/src/" + casename
sub_pred_path = result_path + "/kidney_modify/" + casename
if not os.path.exists(sub_src_path):
os.makedirs(sub_src_path)
if not os.path.exists(sub_pred_path):
os.makedirs(sub_pred_path)
for i in range(np.shape(xs_array)[0]):
cv2.imwrite(sub_src_path + "/" + str(i) + ".bmp", xs_array[i])
cv2.imwrite(sub_pred_path + "/" + str(i) + ".bmp", ys_pd_array[i])