def testSeg():
model_folder_path = r'z:\SuccessfulModel\ProstateSegmentation'
prostate_segmentor = ProstateSegmentation2D()
prostate_segmentor.LoadModel(model_folder_path)
import os
import glob
from MeDIT.SaveAndLoad import LoadNiiData
root_folder = r'z:\Data\CS_ProstateCancer_Detect_multicenter\ToTest\Cui hai pei'
t2_candidate = glob.glob(os.path.join(root_folder, '*t2*tra*Resize.nii'))
if len(t2_candidate) != 1:
print('Check T2 Path')
return
t2_path = r'z:\temp_data\test_data_prostate\301_case2_1.2.840.113619.2.25.4.1415787.1457070159.316\004_AX T2 PROPELLER.nii'
image, _, show_data = LoadNiiData(t2_path,
dtype=np.float32,
is_show_info=True)
predict_data, mask, mask_image = prostate_segmentor.Run(
image, model_folder_path)
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
Imshow3DArray(predict_data)
Imshow3DArray(Normalize01(show_data),
ROI=np.asarray(predict_data > 0.5, dtype=np.uint8))
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 ShowRoi(process_folder):
for case in sorted(os.listdir(process_folder)):
print(case)
case = 'CDM^chen di ming'
case_folder = os.path.join(process_folder, case)
csv_path = os.path.join(case_folder, 'roi.csv')
t2_path = os.path.join(case_folder, 't2.nii')
roi_list = ['roi.nii', 'roi0.nii', 'roi1.nii', 'roi2.nii']
with open(csv_path, 'r') as csvfile:
reader = csv.DictReader(csvfile)
column = [row['ECE'] for row in reader]
print(column)
_, t2, _ = LoadNiiData(t2_path)
Imshow3DArray(Normalize01(t2.transpose((1, 2, 0))))
for roi in roi_list:
roi_path = os.path.join(case_folder, roi)
if not os.path.exists(roi_path):
continue
_, roi, _ = LoadNiiData(roi_path)
try:
Imshow3DArray(Normalize01(t2.transpose((1, 2, 0))),
roi=Normalize01(roi.transpose(1, 2, 0)))
except Exception as e:
print('failed')
def _TestOutput1():
input_list, output_list = ImageInImageOut2D(
r'c:\SharedFolder\ProstateSegment\Input_1_Ouput_1\testing',
input_shape=[160, 160],
batch_size=1,
augment_param=random_2d_augment)
from MeDIT.Visualization import Imshow3DArray
if isinstance(input_list, list):
input1, input2, input3 = input_list[0], input_list[1], input_list[2]
input1 = np.concatenate(
(input1[..., 0], input1[..., 1], input1[..., 2]), axis=2)
input2 = np.concatenate(
(input2[..., 0], input2[..., 1], input2[..., 2]), axis=2)
input3 = np.concatenate(
(input3[..., 0], input3[..., 1], input3[..., 2]), axis=2)
input1 = np.transpose(input1, (1, 2, 0))
input2 = np.transpose(input2, (1, 2, 0))
input3 = np.transpose(input3, (1, 2, 0))
input_show = np.concatenate((input1, input2, input3), axis=0)
roi = np.squeeze(output_list)
roi = np.concatenate((np.zeros_like(roi), roi, np.zeros_like(roi)),
axis=2)
roi = np.concatenate((roi, roi, roi), axis=1)
roi = np.transpose(roi, (1, 2, 0))
Imshow3DArray(input_show, ROI=np.asarray(roi, dtype=np.uint8))
elif not isinstance(output_list, list):
show_input = np.transpose(np.squeeze(input_list), (1, 2, 0))
show_roi = np.transpose(np.squeeze(output_list), (1, 2, 0))
Imshow3DArray(show_input, ROI=np.asarray(show_roi, dtype=np.uint8))
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 TestOneHot():
t2 = np.load(r'D:\ProblemData\StoreFormatData\CAI NING_0007947872\t2.npy')
roi_onehot = np.load(
r'D:\ProblemData\StoreFormatData\CAI NING_0007947872\roi_onehot.npy')
print(type(t2), type(roi_onehot))
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
Imshow3DArray(Normalize01(t2), roi_onehot[..., 0])
Imshow3DArray(Normalize01(t2), roi_onehot[..., 1])
Imshow3DArray(Normalize01(t2), roi_onehot[..., 2])
def testDetect():
# model_folder_path = r'C:\MyCode\MPApp\DPmodel\CSPCaDetection'
model_folder_path = r'Z:\SuccessfulModel\PCaDetection25_2'
# pca_detect = CST2AdcDwiProstateRoiDetect()
pca_detect = CST2AdcDwiDetect2_5D()
pca_detect.LoadModel(model_folder_path)
from MeDIT.SaveAndLoad import LoadNiiData
t2_image, _, t2_data = LoadNiiData(r'z:\Data\CS_ProstateCancer_Detect_multicenter\ToTest\CAO YONG CHENG\004_t2_fse_tra_Resize.nii', dtype=np.float32, is_show_info=True)
dwi_image, _, dwi_data = LoadNiiData(r'z:\Data\CS_ProstateCancer_Detect_multicenter\ToTest\CAO YONG CHENG\007_epi_dwi_tra_CBSF5_NL3_b750_Reg_Resize.nii', dtype=np.float32, is_show_info=True)
adc_image, _, adc_data = LoadNiiData(r'z:\Data\CS_ProstateCancer_Detect_multicenter\ToTest\CAO YONG CHENG\010_epi_dwi_tra_CBSF5_NL3_ADC_Reg_Resize.nii', dtype=np.float32, is_show_info=True)
prostate_roi_image, _, prostate_roi_data = LoadNiiData(r'z:\Data\CS_ProstateCancer_Detect_multicenter\ToTest\CAO YONG CHENG\prostate_roi.nii.gz', dtype=np.uint8)
# dwi_data = dwi_data[..., -1]
# dwi_image = GetImageFromArrayByImage(dwi_data, adc_image)
print(t2_data.shape, adc_data.shape, dwi_data.shape)
pred, _, mask, _ = pca_detect.Run(t2_image, adc_image, dwi_image,
model_folder_path, prostate_roi_image=prostate_roi_image)
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
show_data = np.concatenate((Normalize01(t2_data), Normalize01(dwi_data), Normalize01(adc_data), pred), axis=1)
show_roi = np.concatenate((mask, mask, mask, mask), axis=1)
show_prostate_roi = np.concatenate((prostate_roi_data, prostate_roi_data, prostate_roi_data), axis=1)
Imshow3DArray(show_data, ROI=[show_roi, show_prostate_roi])
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_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 ShowData():
from MeDIT.SaveAndLoad import LoadNiiData
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
import matplotlib.pyplot as plt
folder = r'Z:\RawData\TZ_ROI_20191119\prostate segmentation _PZ_TZ\Nii'
case_list = os.listdir(folder)
for case in case_list:
print(case)
case_path = os.path.join(folder, case)
t2_path = os.path.join(case_path, 't2_Resize.nii')
pz_path = os.path.join(case_path, 'ROI_jkw0.nii')
cg_path = os.path.join(case_path, 'ROI_jkw1.nii')
if not os.path.exists(cg_path):
print('cg not exists')
continue
if not os.path.exists(pz_path):
print('pz not exists')
continue
_, _, t2_data = LoadNiiData(t2_path,
dtype=np.float32,
is_show_info=False)
_, _, pz_data = LoadNiiData(pz_path,
dtype=np.float32,
is_show_info=False)
_, _, cg_data = LoadNiiData(cg_path,
dtype=np.float32,
is_show_info=False)
Imshow3DArray(Normalize01(t2_data), roi=[pz_data, cg_data])
def ModelPre():
import numpy as np
from CNNModel.SuccessfulModel.ProstateSegment import ProstateSegmentationTrumpetNet
# from MeDIT.UsualUse import *
from MeDIT.SaveAndLoad import LoadNiiData
from MeDIT.Normalize import Normalize01
from MeDIT.Visualization import Imshow3DArray
model_path = r'd:\SuccessfulModel\ProstateSegmentTrumpetNet'
segment = ProstateSegmentationTrumpetNet()
if not segment.LoadConfigAndModel(model_path):
print('Load Failed')
t2_image, _, t2 = LoadNiiData(
r'd:\Data\HouYing\processed\BIAN JIN YOU\t2.nii')
preds, mask, mask_image = segment.Run(t2_image)
show_data = np.concatenate(
(Normalize01(t2), np.clip(preds, a_min=0.0, a_max=1.0)), axis=1)
show_roi = np.concatenate((mask, mask), axis=1)
Imshow3DArray(show_data, roi=show_roi)
def get_3D_array(self):
T_2_array = []
file_list = self.sort_file(self.store_folder_path)
for file in reversed(file_list):
file_path = os.path.join(self.store_folder_path, file)
image_2D_array = np.load(file_path)
T_2_array.append(image_2D_array)
T_2_array = np.reshape(T_2_array, self.roi_array.shape)
roi = np.transpose(self.roi_array, (1, 2, 0))
T_2_array = np.transpose(T_2_array, (1, 2, 0))
Imshow3DArray(T_2_array, roi)
# for i in range(20):
T_2_array_2D = T_2_array[:, :, 19]
roi_array_2D = roi[:, :, 19]
ax = sns.heatmap(T_2_array_2D)
plt.show()
mean_roi = []
for i in np.reshape(
T_2_array_2D * roi_array_2D,
(self.roi_array.shape[1] * self.roi_array.shape[2])):
if i != 0:
mean_roi.append(i)
print(np.mean(mean_roi))
plt.hist(mean_roi, bins=40)
plt.show()
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 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 show_T2(root_path):
"""查看一下图像"""
for case in os.listdir(root_path):
T2_array = get_array_from_path(os.path.join(root_path, case, "T2.nii"))
roi_array = get_array_from_path(
os.path.join(root_path, case, "ruan_gu_up_1.nii"))
T2_array = standard(T2_array)
Imshow3DArray(T2_array, roi_array)
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 show(self):
not_t2_data = r"E:\split_data_ACLR_T2\ZHANG YU CHENG-20190601\data_1.nii"
data = sitk.ReadImage(not_t2_data)
data_array = sitk.GetArrayFromImage(data)
data_array = np.transpose(data_array, (1, 2, 0))
print(self.roi_array.shape)
print(data_array.shape)
roi = np.transpose(self.roi_array, (1, 2, 0))
Imshow3DArray(data_array, roi)
def TestResampleData():
roi_path = r'X:\PrcoessedData\ProstateCancerECE\ZJ^zhang jian\roi.nii'
t2_path = r'X:\PrcoessedData\ProstateCancerECE\ZJ^zhang jian\t2.nii'
dwi_path = r'X:\PrcoessedData\ProstateCancerECE\ZJ^zhang jian\dwi.nii'
adc_path = r'X:\PrcoessedData\ProstateCancerECE\ZJ^zhang jian\adc.nii'
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
_, _, t2 = LoadImage(t2_path, is_show_info=True)
_, _, dwi = LoadImage(dwi_path, is_show_info=True)
img, _, adc = LoadImage(adc_path, is_show_info=True)
_, _, roi = LoadImage(roi_path, dtype=np.uint8, is_show_info=True)
#
new_dwi = dwi[..., 2]
new_img = sitk.GetImageFromArray(new_dwi)
new_img.SetDirection(img.GetDirection())
new_img.SetSpacing(img.GetSpacing())
new_img.SetOrigin(img.GetOrigin())
sitk.WriteImage(
new_img,
os.path.join(r'X:\PrcoessedData\ProstateCancerECE\ZJ^zhang jian',
'max_dwi.nii'))
#
Imshow3DArray(Normalize01(t2), roi=Normalize01(roi))
Imshow3DArray(Normalize01(dwi))
# Imshow3DArray(Normalize01(adc))
# TestResampleData()
######################################################
# for case in os.listdir(r'X:\PrcoessedData\BCR-ECE-score'):
# case_folder = os.path.join(r'X:\PrcoessedData\BCR-ECE-score', case)
# if len(os.listdir(case_folder)) < 1:
# shutil.copytree(os.path.join(r'X:\RawData\BCR-ECE-score\BCR-ECE-score', case), os.path.join(r'C:\Users\ZhangYihong\Desktop\aaaa', case))
# for case in os.listdir(r'C:\Users\ZhangYihong\Desktop\aaaa\NoPath'):
# if case not in os.listdir(r'X:\StoreFormatData\ProstateCancerECE\ResampleData'):
# print(case)
#
# t2_1 = sitk.GetArrayFromImage(sitk.ReadImage(r'X:\RawData\BCR-ECE-score\BCR-ECE-score\CSJ^chen shi jie\dwi.nii'))
# t2_2 = sitk.GetArrayFromImage(sitk.ReadImage(r'C:\Users\ZhangYihong\Desktop\aaaa\OriginalPath\CSJ^chen shi jie\dwi.nii'))
# print((t2_2 == t2_1).all())
def MultiRoi():
from MeDIT.SaveAndLoad import LoadNiiData
data_folder = r'X:\PrcoessedData\ProstateCancerECE'
multi_list = [
'CHEN REN', 'CHEN ZHENG', 'DING YONG MING', 'DU KE BIN',
'FYK^fan yuan kai', 'GAO FA MING', 'GCD^gu chuan dao',
'GCF^gao chang fu', 'GENG LONG XIANG^GENG LONG XIANG',
'GSH^gao si hui', 'GU SI KANG', 'HFL^he fu lin', 'HONG QI YUN',
'JCG^jiang cheng guang', 'JIANG TIAN CAI^JIANG TIAN CAI',
'JJS^jin ju sheng', 'JZL^ji zhao lan', 'LCD^li cong de',
'LCZ^li chao zhi', 'LDJ^lei dao jin', 'LI JIA LIN',
'LLT^liang li tong', 'LRH^lu ronghua', 'MPX^ma pei xi',
'PENG XIAN XIANG', 'QB^qian bin', 'SBQ^shao ba qian',
'SHA GUANG YI^SHA GUANGYI', 'SHEN DAO XIANG', 'SSS^tang shan sheng',
'SUN BING LIANG', 'SUN QING ZHI', 'SUN ZHANG BAO', 'SXC^su xue cao',
'WANG YONG ZHENG', 'WEI CHANG HUA', 'WLB^wu lu bao',
'WLJ^wu liang ju ^13815870351^6924-31', 'WRM^weng rong ming',
'WU XIAO LEI', 'WZC^wu zhi chao', 'XJJ^xu jianjun',
'XJS^xu jingshan ^^6875-+04', 'XNB^xu neng bao', 'XSL^xu sen lou',
'XWC^xia wen cai', 'YANG YONG JIU', 'YFG^yang fu gang',
'YHX^yang hong xiao', 'YRE^yang rong r', 'YRF^yan rong fa',
'YU TU JUN', 'YYX^yin yong xing', 'ZGF^zhu guo fang', 'ZHAO YU LONG',
'ZHP^zhang heping', 'ZMJ^zhao mao jin', 'ZOU SHOU ZHONG',
'ZXJ^zhou xian jin ^^6698+5', 'ZXM^zhou xinmin', 'ZXT^zhu xiao ting',
'ZZF^zhou zheng fang ^^6698', 'ZZQ^zhou zu quan'
]
print(len(multi_list))
for a, case in enumerate(multi_list):
data_path = os.path.join(data_folder, case)
# if not os.path.exists(data_path):
# print(case)
# else:
# data_list = os.listdir(data_path)
# roi_dict = {}
# for info in data_list:
# roi_dict['name'] = case
# if 'roi' in info and '.nii' in info:
# roi_dict[data_list.index(info)] = info
# roi_path = os.path.join(data_path, info)
# _, roi, _ = LoadNiiData(roi_path)
# label_im, nb_labels = ndimage.label(roi)
# for index in range(1, nb_labels+1):
# num = str(label_im.tolist()).count(str(index))
# if num >= 10:
# roi_dict[index] = num
# else:
# continue
# print(a, roi_dict)
roi_path = os.path.join(data_path, 'roi.nii')
t2_path = os.path.join(data_path, 't2.nii')
_, _, roi = LoadNiiData(roi_path)
_, _, t2 = LoadNiiData(t2_path)
label_im, nb_labels = ndimage.label(roi)
Imshow3DArray(Normalize01(t2), roi=Normalize01(label_im))
def show(self):
not_t2_data = r"E:\split_data_ACLR_T2\RUAN XIAO BAI-20140430\data_1.nii"
data = sitk.ReadImage(not_t2_data)
data_array = sitk.GetArrayFromImage(data)
data_array = np.transpose(data_array, (1, 2, 0))
# roi = np.transpose(self.roi_array, (1, 2, 0))
data_array = self.stand(data_array)
data_array = np.flipud(data_array)
Imshow3DArray(data_array, self.roi_array)
def save_moved_roi(self):
roi_array = td.get_array(self.roi_path)
roi_4_array = np.where(roi_array == 4, 1, 0)
data_array = td.get_array(self.data_path)
data_array = td.standard(data_array)
data_array = np.flipud(data_array)
new_roi_array = self.move_roi(roi_4_array)
# SaveNumpyToImageByRef(store_path=os.path.join(self.case_path, "moved_roi_4.nii"), data=new_roi_array,
# ref_image=self.data)
Imshow3DArray(data_array, [roi_4_array, new_roi_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 ShowProblemData(resample_folder, case):
case_path = os.path.join(resample_folder, case)
t2_path = os.path.join(case_path, 't2.nii')
roi_path = os.path.join(case_path, 'roi.nii')
dwi_path = os.path.join(case_path, 'dwi_Reg.nii')
adc_path = os.path.join(case_path, 'adc_Reg.nii')
# Load data
_, t2, _ = LoadNiiData(t2_path, dtype=np.float32)
_, dwi, _ = LoadNiiData(dwi_path, dtype=np.float32)
_, adc, _ = LoadNiiData(adc_path, dtype=np.float32)
_, roi, _ = LoadNiiData(roi_path, dtype=np.uint8)
t2 = np.transpose(t2, [1, 2, 0])
dwi = np.transpose(dwi, [1, 2, 0])
adc = np.transpose(adc, [1, 2, 0])
_, _, new_roi = KeepLargest(roi)
new_roi = np.transpose(new_roi, [1, 2, 0])
Imshow3DArray(Normalize01(t2), roi=Normalize01(new_roi))
Imshow3DArray(Normalize01(dwi), roi=Normalize01(new_roi))
Imshow3DArray(Normalize01(adc), roi=Normalize01(new_roi))
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 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_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 TestMergePzAndTz():
merge_path = r'Z:\StoreFormatData\PzTzSegment_ZYH\ZYHDraw\2019-CA-formal-BAO TONG\merge_pz1_cg2.nii'
t2_path = r'Z:\StoreFormatData\PzTzSegment_ZYH\ZYHDraw\2019-CA-formal-BAO TONG\t2.nii'
pz_path = r'Z:\StoreFormatData\PzTzSegment_ZYH\ZYHDraw\2019-CA-formal-BAO TONG\pz.nii'
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
_, _, t2 = LoadNiiData(t2_path)
_, _, pz = LoadNiiData(pz_path)
_, _, merge_roi = LoadNiiData(merge_path, dtype=np.uint8)
Imshow3DArray(
np.concatenate((Normalize01(pz), Normalize01(merge_roi)), axis=1))
def TestResampleData():
merge_path = r'D:\ProblemData\StoreFormatData\CAI NING_0007947872\MergeRoi_Resize_05x05.nii'
t2_path = r'D:\ProblemData\StoreFormatData\CAI NING_0007947872\t2_Resize_05x05.nii'
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
_, _, t2 = LoadNiiData(t2_path, is_show_info=True)
_, _, merge_roi = LoadNiiData(merge_path,
dtype=np.uint8,
is_show_info=True)
Imshow3DArray(
np.concatenate((Normalize01(t2), Normalize01(merge_roi)), axis=1))
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])
