def get_croped_data_roi(data_path, roi_path, store_path=None):
data_array = get_array_from_path(data_path)
# data_array = np.flipud(data_array) # 注意这里的数据要进行翻转的
roi_array = get_array_from_path(roi_path)
data_array = standard(data_array)
crop_array = crop_data_array(roi_array, data_array)
def resize_data(data_array, resized_shape):
resized_data = cv2.resize(data_array,
resized_shape,
interpolation=cv2.INTER_NEAREST)
return resized_data
roi_new = crop_data_array(roi_array, roi_array)
new_array = resize_data(crop_array, (224, 224))
roi_new = resize_data(roi_new, (224, 224))
# 选择只含有roi的层进行保存
index = []
for i in range(roi_new.shape[-1]):
if np.sum(roi_new[..., i]) != 0:
index.append(i)
roi_new = roi_new[..., index]
data_new = new_array[..., index]
Imshow3DArray(data_new, roi_new)
def get_result():
data_path = r"Y:\DYB\shidaoai_vibenii\649302\BLADE42_256_3mm\026_BLADE42_256_3mm.nii"
roi_path = r"Y:\DYB\shidaoai_vibenii\649302\BLADE42_256_3mm\026_BLADE42_256_3mm_ROI.mha"
data_array = standard(get_array_from_path(data_path))
roi_array = get_array_from_path(roi_path)
new_roi = binary_dilation(roi_array)
Imshow3DArray(data_array, [roi_array, new_roi*1])
def show_origin_roi(self, value):
"""展示最原始的ROI，并去掉半月板"""
for case in os.listdir(self.root_path):
data_path = os.path.join(self.root_path, case, "new_data.nii")
data_array = standard(get_array_from_path(data_path))
roi_path = os.path.join(self.root_path, case, "roi.nii.gz")
"""展示原始ROI"""
roi_array = get_array_from_path(roi_path)
roi_list = self.get_single_roi(roi_array)
# Imshow3DArray(data_array, roi_list)
"""展示软骨和下骨ROI"""
bone_roi_path = os.path.join(self.root_path, case,
"moved_new_roi.nii.gz") # 整数
bone_roi_array = get_array_from_path(bone_roi_path)
bone_roi_list = self.get_single_roi(bone_roi_array)
Imshow3DArray(data_array, bone_roi_list[value])
Imshow3DArray(data_array, roi_list[value])
print(np.unique(bone_roi_array))
"""展示计算图像"""
four_array = os.path.join(self.root_path, case, "four_T2_array",
"T2_3D_array.npy")
four_array = np.load(four_array)
img_mean = cv2.blur(four_array, (5, 5))
# for i in range(four_array.shape[-1]):
# sns.set()
# ax = sns.heatmap(img_mean[...,i])
# plt.show()
break
def show_3d_roi():
case_path = r"Y:\DYB\data_and_result\doctor tao\data\all_data\Normal control\10007807"
ruan_roi_list = ["new_ruan_roi_" + str(i) for i in range(1, 8)]
xia_roi_list = ["roi_" + str(i) for i in range(1, 8)]
ruan_roi_path_list = [os.path.join(case_path, i) for i in ruan_roi_list]
xia_roi_path_list = [os.path.join(case_path, i) for i in xia_roi_list]
ruan_roi_array_list = [get_array_from_path(i) for i in ruan_roi_path_list]
xia_roi_array_list = [get_array_from_path(i) for i in xia_roi_path_list]
data_array = get_array_from_path(os.path.join(case_path, "data.nii"))
def show():
"""经过显示发现图像并不是很好，可能需要配准一下"""
root_path = r"Y:\DYB\2020832DATA\doctor_gao\pet_ct_egfr\EGFR-"
for case in os.listdir(root_path):
ct_path = os.path.join(root_path, case, "ct.nii")
pet_path = os.path.join(root_path, case, "pet.nii")
roi_path = os.path.join(root_path, case, "no_name.nii")
ct_array = get_array_from_path(ct_path)
pet_array = get_array_from_path(pet_path)
roi_array = get_array_from_path(roi_path)
Imshow3DArray(standard(ct_array), roi_array)
Imshow3DArray(standard(pet_array), roi_array)
def check_resample_result(self):
"""检查重采样的结果"""
resample_result = os.path.join(self.case_path, "pet_Resize.nii")
pet_array = get_array_from_path(resample_result)
sum_array = np.sum(pet_array)
pet_array = standard(pet_array)
roi_path = os.path.join(self.case_path, "no_name.nii")
roi_array = get_array_from_path(roi_path)
pet_array = np.flipud(pet_array)
Imshow3DArray(pet_array, roi_array)
print(sum_array)
def check_data():
case_path = r"Y:\DYB\PETCT_EGFR\EGFR+\004+"
ct_path = os.path.join(case_path, "ct.nii")
pet_path = os.path.join(case_path, "pet_Resize.nii")
roi_path = os.path.join(case_path, "ct_roi.nii")
ct_array = get_array_from_path(ct_path)
pet_array = get_array_from_path(pet_path)
roi_array = get_array_from_path(roi_path)
ct_array = standard(ct_array)
pet_array = standard(pet_array)
pet_array = np.flipud(pet_array)
Imshow3DArray(ct_array, roi_array)
def show_T2_and_ROI(self):
T2_path = os.path.join(self.case_path, "T2.nii")
roi_list = [
"xia_gu_down_1.nii", "xia_gu_down_2.nii", "xia_gu_up_1.nii",
"xia_gu_up_2.nii"
]
T2_array = standard(get_array_from_path(T2_path))
T2_array = cv2.blur(T2_array, (5, 5))
for roi in roi_list:
roi_path = os.path.join(self.case_path, roi)
roi_array = get_array_from_path(roi_path)
Imshow3DArray(T2_array, roi_array)
def get_roi(self):
for group in self.normal_and_instability:
for case in os.listdir(os.path.join(self.root_path, group)):
data_path = os.path.join(self.root_path, group, case, "data.nii")
data_array = standard(get_array_from_path(data_path))
data = sitk.ReadImage(data_path)
big_roi = 0
for i in range(1, 9):
roi_path = os.path.join(self.root_path, group, case, "roi_" + str(i) + ".nii")
roi_array = get_array_from_path(roi_path)
big_roi += roi_array
Imshow3DArray(data_array, big_roi)
def show_resample_image():
data_path = r"Y:\DYB\PETCT_EGFR\EGFR-\001-\pet_Resize.nii"
roi_path = r"Y:\DYB\PETCT_EGFR\EGFR-\001-\no_name.nii"
roi_array = get_array_from_path(roi_path)
data_array = get_array_from_path(data_path)
data_array = np.flipud(data_array)
ct_path = r"Y:\DYB\PETCT_EGFR\EGFR-\001-\ct.nii"
ct_array = get_array_from_path(ct_path)
ct_pet_array = ct_array + data_array
ct_pet_array = standard(ct_pet_array)
ct_pet_array = np.flipud(ct_pet_array)
ct_array = np.flipud(ct_array)
# Imshow3DArray(standard(ct_array), roi_array)
Imshow3DArray(ct_pet_array, roi_array)
def check_roi():
"""检查之前ROI中的数值"""
case_path = r"Y:\DYB\data_and_result\doctor_xie\knee_data\CAI NAI JI-20140605"
data_path = os.path.join(case_path, "data_1.nii")
roi_path = os.path.join(case_path, "roi.nii.gz")
data_array = standard(get_array_from_path(data_path))
data_array = np.flipud(data_array)
roi_array = get_array_from_path(roi_path)
roi_1 = np.where(roi_array == 1, 1, 0) # 上软骨后面的层
roi_2 = np.where(roi_array==2, 1, 0) # 上软骨前面的层
roi_3 = np.where(roi_array==3, 1, 0) # 下软骨后面的层
roi_4 = np.where(roi_array==4, 1, 0)
roi_5 = np.where(roi_array==5, 1, 0)
roi_6 = np.where(roi_array==6, 1, 0)
Imshow3DArray(standard(data_array), [roi_1, roi_2, roi_3, roi_4])
def get_T2_ninety(self, roi_name):
roi_array = get_array_from_path(
os.path.join(self.case_path, roi_name + ".nii"))
old_array = self.T2_array * roi_array
array = np.unique(old_array)
ninety_value = np.percentile(array, 90)
return ninety_value
def get_T2_mean(self, roi_name): # 对应软骨名称
roi_array = get_array_from_path(
os.path.join(self.case_path, roi_name + ".nii"))
old_array = self.T2_array * roi_array
array = old_array.flatten()
new_array = [i for i in array if i != 0]
return sum(new_array) / len(new_array)
def get_image(self):
"""
该函数用于：存放计算的三维T2 四回波
:return:
"""
for case in os.listdir(self.root_path):
store_data_path = os.path.join(self.root_path, case, "T2_array")
new_store_data = self.sorted_file(folder_path=store_data_path)
roi_path = os.path.join(self.root_path, case, "xia_gu_down_1.nii") # 原始ROI
roi_array = get_array_from_path(roi_path) # 将ROI的形状作为参考，以及保存为nii的参考图像
ref_roi = sitk.ReadImage(roi_path)
T_2_array = [] # 三维
for file in reversed(new_store_data): # 这里进行反转的原因感觉是因为reshape方法的问题
file_path = os.path.join(self.root_path, case, "T2_array", file)
image_2D = np.load(file_path)
T_2_array.append(image_2D)
change_roi = np.transpose(roi_array, (2, 1, 0))
T_2_array = np.reshape(T_2_array, change_roi.shape)
T_2_array = np.transpose(T_2_array, (1, 2, 0))
import cv2
T_2_array_filtered = cv2.blur(T_2_array, (5,5))
T_2_array_filtered = np.asarray(T_2_array_filtered)
# print(T_2_array)
# SaveNumpyToImageByRef(os.path.join(self.root_path, case, "T2_filtered.nii"), T_2_array_filtered, ref_roi)
SaveNumpyToImageByRef(os.path.join(self.root_path, case, "T2.nii"), T_2_array, ref_roi)
print("case {} is saved".format(case))
def get_data_array(self):
for file in os.listdir(self.case_path):
if file.split("_")[-1] == "src.nii":
data_path = os.path.join(self.case_path, file)
data_array = get_array_from_path(data_path)
data_array = standard(data_array)
return data_array
def show(self):
for file in os.listdir(self.case_path):
if file.split("_")[-1] == "src.nii":
data_path = os.path.join(self.case_path, file)
data_array = standard(get_array_from_path(data_path))
roi_array_list = self.get_roi_list()
Imshow3DArray(data_array, roi_array_list)
def get_roi():
root_path = r"Y:\DYB\2020832DATA\doctor_xie\Normal_data_nii_and_T2"
for case in os.listdir(root_path)[4:]:
case_path = os.path.join(root_path, case)
data_path = os.path.join(case_path, "data.nii")
data = sitk.ReadImage(data_path)
data_array = get_array_from_path(data_path)
data_array = standard(data_array)
data_array = np.flipud(data_array)
roi_name = ["roi_" + str(i) + ".npy"for i in range(1, 5)]
roi_path = [os.path.join(case_path, i) for i in roi_name]
roi_array_list = [np.load(i) for i in roi_path]
# roi_array_list = [get_big_roi(i) for i in roi_array_list]
roi_1 = np.where(roi_array_list[0] == 1, 1, 0)
roi_2 = np.where(roi_array_list[1] == 1, 2, 0)
roi_3 = np.where(roi_array_list[2] == 1, 3, 0)
roi_4 = np.where(roi_array_list[3] == 1, 4, 0)
# roi_1 = get_big_roi(roi_1)
# roi_2 = get_big_roi(roi_2)
# roi_3 = get_big_roi(roi_3)
# roi_4 = get_big_roi(roi_4)
# big_roi = roi_1 + roi_2 + roi_3 + roi_4
# print(np.sum(roi_1), np.sum(roi_2), np.sum(roi_3), np.sum(roi_4))
Imshow3DArray(data_array, roi_array_list)
# SaveArrayToNiiByRef(store_path = os.path.join(case_path, "roi.nii.gz"),
# array=big_roi,
# ref_image=data)
print(case)
def get_image():
data_path = r"Y:\DYB\chang\BAO CAI ZHEN_2769351_sag_Resample.nii"
data_array = get_array_from_path(data_path)
data_array = standard(data_array)
th = filters.threshold_otsu(data_array)
roi_array = np.where(data_array > th / 6.1, 1, 0)
roi_array = cv2.blur(roi_array, (12, 12))
Imshow3DArray(data_array, roi_array)
def get_data_roi():
root_path = r"Y:\DYB\PETCT_EGFR"
for folder in os.listdir(root_path):
folder_path = os.path.join(root_path, folder)
for case in os.listdir(folder_path):
case_path = os.path.join(folder_path, case)
ct_path = os.path.join(case_path, "ct.nii")
roi_path = os.path.join(case_path, "no_name.nii")
data = sitk.ReadImage(ct_path)
ct_array = get_array_from_path(ct_path)
roi_array = get_array_from_path(roi_path)
roi_array = np.flipud(roi_array)
SaveArrayToNiiByRef(store_path=os.path.join(
case_path, "ct_roi.nii"),
array=roi_array,
ref_image=data)
print(case)
def get_2d_roi():
root_path = r"Y:\LS\liusheng _shidaoai_train_and_test"
store_path = r"Y:\LS\xxxxxxxxxxxx"
for folder in os.listdir(root_path):
folder_path = os.path.join(root_path, folder)
store_folder_path = make_folder(os.path.join(store_path, folder))
for case in os.listdir(folder_path):
case_path = os.path.join(folder_path, case)
data_path = os.path.join(case_path, "data.nii.gz")
roi_path = os.path.join(case_path, "ROI.nii.gz")
data_array = get_array_from_path(data_path)
roi_array = get_array_from_path(roi_path)
# 存放一个方向的结果
for i in range(roi_array.shape[-1]):
if np.sum(roi_array[..., i]) != 0:
# 开始存放：
# data 1 是从一个方向看的结果
data_1 = data_array[..., i-1:i+2]
roi_1 = roi_array[..., i-1:i+2]
store_case_path = make_folder(os.path.join(store_folder_path, str(i)+"_1_"+case))
np.save(os.path.join(store_case_path, "data.npy"), data_1)
np.save(os.path.join(store_case_path, "roi.npy"), roi_1)
print("---1---")
# 存放另一个方向的结果
for j in range(roi_array.shape[0]):
if np.sum(roi_array[j, ...]) != 0:
# 开始存放
# data 2 是从第二个方向看的结果
data_2 = data_array[j-1:j+2, ...]
roi_2 = roi_array[j-1:j+2, ...]
store_case_path = make_folder(os.path.join(store_folder_path, str(j) + "_2_" + case))
np.save(os.path.join(store_case_path, "data.npy"), data_2)
np.save(os.path.join(store_case_path, "roi.npy"), roi_2)
print("---2---")
# 存放第三个方向看的结果
for k in range(roi_array.shape[1]):
if np.sum(roi_array[:, k, :]) != 0:
# 开始存放第三个方向的结果 data 3
data_3 = data_array[:, k-1:k+2, :]
roi_3 = roi_array[:, k-1:k+2, :]
store_case_path = make_folder(os.path.join(store_folder_path, str(k) + "_3_" + case))
np.save(os.path.join(store_case_path, "data.npy"), data_3)
np.save(os.path.join(store_case_path, "roi.npy"), roi_3)
print("---3---")
print("case {} is finished!".format(case))
def get_new_pet(root_path):
for case in os.listdir(root_path):
pet_path = os.path.join(root_path, case, "pet.nii")
ct_path = os.path.join(root_path, case, "ct.nii")
roi_path = os.path.join(root_path, case, "no_name.nii")
roi_array = get_array_from_path(roi_path)
reg, result = Registrator_image(fix_path=ct_path, moving_path=pet_path)
Imshow3DArray(standard(reg), roi_array)
def __init__(self, dicom_path, roi_path, store_path):
self.dicom_path = dicom_path # 一个case, 存放dicom文件的路径
self.roi_path = roi_path
self.roi_array = get_array_from_path(self.roi_path)
self.roi = sitk.ReadImage(self.roi_path)
self.store_path = store_path
self.oirgin_roi_array = sitk.GetArrayFromImage(
sitk.ReadImage(self.roi_path))
print(self.oirgin_roi_array.shape)
def get_roi_array_list(self):
roi_array_list = []
roi_name_list = []
for file in os.listdir(self.all_roi_path):
roi_path = os.path.join(self.all_roi_path, file)
roi_array = get_array_from_path(roi_path)
roi_array_list.append(roi_array)
roi_name_list.append(file)
return roi_array_list, roi_name_list
def get_roi_array(self):
roi_array_list = []
for file in os.listdir(self.case_path):
if file.split("_")[-1] != "src.nii":
roi_path = os.path.join(self.case_path, file)
print(roi_path)
roi_array = get_array_from_path(roi_path)
roi_array_list.append(roi_array)
return roi_array_list
def check_data_roi():
"""检查数据和ROI"""
root_path = r"Y:\DYB\data_and_result\doctor_xie\knee_data"
store_path = r"Y:\DYB\data_and_result\doctor_xie\knn_segment_all_roi"
for case in os.listdir(root_path):
case_path = os.path.join(root_path, case)
store_case_path = make_folder(os.path.join(store_path, case))
# 选择分割的数据是的一个回波的数据
data_path = os.path.join(case_path, "data_1.nii")
roi_path = os.path.join(case_path, "roi.nii.gz")
data_array = standard(get_array_from_path(data_path))
data_array = np.flipud(data_array)
roi_array = get_array_from_path(roi_path)
roi_array_list = [
np.where(roi_array == i, 1, 0) for i in np.unique(roi_array)[1:]
]
np.save(os.path.join(store_case_path, "data.npy"), data_array)
np.save(os.path.join(store_case_path, "all_roi.npy"), roi_array_list)
print(case, " is finished!")
def show_T2_mampping(self):
t2_mapping_path = os.path.join(self.case_path, "new_T2.nii")
t2_mapping_array = get_array_from_path(t2_mapping_path)
# sns.set()
# sns.heatmap(t2_mapping_array[..., 5])
# plt.axis('off')
# plt.show()
ruan_roi_path = r"Y:\DYB\data_and_result\doctor_xie\knee_data\CAI NAI JI-20140605\ruan_gu_down_1.nii"
xia_roi_path = r"Y:\DYB\data_and_result\doctor_xie\knee_data\CAI NAI JI-20140605\xia_gu_down_1.nii"
ruan_array = get_array_from_path(ruan_roi_path)
xia_array = get_array_from_path(xia_roi_path)
for i in range(t2_mapping_array.shape[-1]):
if np.sum(ruan_array[..., i]) != 0 and np.sum(xia_array[...,
i]) != 0:
sns.heatmap(t2_mapping_array[..., i])
plt.contour(ruan_array[..., i], colors="blue", linewidths=0.45)
plt.contour(xia_array[..., i], linewidths=0.45)
plt.axis('off')
plt.show()
def get_roi_list(self):
roi_array_list = []
roi_name = [
"roi_1.nii", "roi_2.nii", "roi_3.nii", "roi_4.nii", "roi_5.nii",
"roi_6.nii", "roi_7.nii", "roi_8.nii"
]
for name in roi_name:
roi_path = os.path.join(self.case_path, name)
roi_array = get_array_from_path(roi_path)
roi_array_list.append(roi_array)
return roi_array_list
def get_croped_data_roi(data_path, roi_path, store_path):
data_array = get_array_from_path(data_path)
# data_array = np.flipud(data_array) # 注意这里的数据要进行翻转的
roi_array = get_array_from_path(roi_path)
data_array = standard(data_array)
crop_array = crop_data_array(roi_array, data_array)
def resize_data(data_array, resized_shape):
resized_data = cv2.resize(data_array,
resized_shape,
interpolation=cv2.INTER_NEAREST)
return resized_data
roi_new = crop_data_array(roi_array, roi_array)
new_array = resize_data(crop_array, (224, 224))
roi_new = resize_data(roi_new, (224, 224))
# Imshow3DArray(standard(new_array), roi_new)
np.save(os.path.join(store_path, "croped_pet.npy"), new_array)
np.save(os.path.join(store_path, "croped_roi.npy"), roi_new)
def show_knee_array(self):
five_echo_name_list = ["data_" + str(i) for i in range(1, 6)]
five_echo_path_list = [
os.path.join(self.case_path, i) for i in five_echo_name_list
]
five_array_list = [
np.flipud(get_array_from_path(i)) for i in five_echo_path_list
]
# # 显示五个回波的图像
for echo, i in zip(five_array_list[1:], range(1, 6)):
plt.subplot(4, 1, i)
self.show_array(echo)
plt.tight_layout(pad=0.5, h_pad=0.5, w_pad=0.5)
plt.show()
def get_ROI(self, value, store_name): # value用来选择
ruangu_roi = os.path.join(self.case_path, "roi.nii.gz") # 软骨路径
xiagu_roi = os.path.join(self.case_path,
"moved_new_roi.nii.gz") # 下骨路径
data_path = os.path.join(self.case_path, "data_1.nii")
data_array = standard(np.flipud(get_array_from_path(data_path)))
data = sitk.ReadImage(data_path)
ruangu_roi_array = get_array_from_path(ruangu_roi)
single_ruangu_array = np.where(ruangu_roi_array == value, 1, 0) # 软骨
xiagu_roi_array = get_array_from_path(xiagu_roi)
single_xiagu_array = np.where(xiagu_roi_array == value, 1, 0) # 下骨
# 显示检查
# Imshow3DArray(data_array, [single_ruangu_array, single_xiagu_array])
SaveArrayToNiiByRef(
os.path.join(self.case_path, "ruan_gu_" + store_name + ".nii"),
single_ruangu_array, data)
SaveArrayToNiiByRef(
os.path.join(self.case_path, "xia_gu_" + store_name + ".nii"),
single_xiagu_array, data)
