def MergePzAndTz():
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
root_folder = r'D:\ProblemData\Prcoessed'
for case in os.listdir(root_folder):
case_folder = os.path.join(root_folder, case)
if not os.path.isdir(case_folder):
continue
print('Estimating PZ of {}'.format(case))
cg_path = os.path.join(case_folder, 'cg.nii')
pz_path = os.path.join(case_folder, 'pz.nii')
cg_image, _, cg_data = LoadNiiData(cg_path, dtype=np.uint8)
pz_image, _, pz_data = LoadNiiData(pz_path, dtype=np.uint8)
# Merge
merge_roi = pz_data + 2 * cg_data
# Imshow3DArray(np.concatenate((Normalize01(cg_data), Normalize01(pz_data), Normalize01(merge_roi)), axis=1))
# print(np.unique(merge_roi))
# Save PZ image
merge_image = GetImageFromArrayByImage(merge_roi, pz_image)
SaveNiiImage(os.path.join(case_folder, 'merge_pz1_cg2.nii'),
merge_image)
def OneHot():
root_folder = r'D:\ProblemData\StoreFormatData'
for case in os.listdir(root_folder):
case_folder = os.path.join(root_folder, case)
if not os.path.isdir(case_folder):
continue
print('Onthot coding: {}'.format(case))
t2_path = os.path.join(case_folder, 't2_Resize_05x05.nii')
roi_path = os.path.join(case_folder, 'MergeRoi_Resize_05x05.nii')
_, _, t2 = LoadNiiData(t2_path)
_, _, merge_roi = LoadNiiData(roi_path, dtype=np.uint8)
# One Hot
# 这里3代表PZ, CG, 背景, output : row x column x slices x 3
output = np.zeros(
(merge_roi.shape[0], merge_roi.shape[1], merge_roi.shape[2], 3),
dtype=np.uint8)
output[..., 0] = np.asarray(merge_roi == 0,
dtype=np.uint8) # save background
output[..., 1] = np.asarray(merge_roi == 1, dtype=np.uint8) # save PZ
output[..., 2] = np.asarray(merge_roi == 2, dtype=np.uint8) # save CG
np.save(os.path.join(case_folder, 't2.npy'), t2)
np.save(os.path.join(case_folder, 'roi_onehot.npy'), output)
def ResampleData():
from MIP4AIM.NiiProcess.Resampler import Resampler
root_folder = r'D:\ProblemData\Prcoessed'
dest_root = r'D:\ProblemData\StoreFormatData'
resampler = Resampler()
for case in os.listdir(root_folder):
case_folder = os.path.join(root_folder, case)
if not os.path.isdir(case_folder):
continue
dest_case_folder = os.path.join(dest_root, case)
if not os.path.exists(dest_case_folder):
os.mkdir(dest_case_folder)
print('Resample PZ of {}'.format(case))
t2_path = os.path.join(case_folder, 't2.nii')
merge_path = os.path.join(case_folder, 'merge_pz1_cg2.nii')
t2_image, _, t2_data = LoadNiiData(t2_path)
merge_image, _, merge_data = LoadNiiData(merge_path, dtype=np.uint8)
resampler.ResizeSipmleITKImage(t2_image,
expected_resolution=[0.5, 0.5, -1],
store_path=os.path.join(
dest_case_folder,
't2_Resize_05x05.nii'))
resampler.ResizeSipmleITKImage(merge_image,
is_roi=True,
expected_resolution=[0.5, 0.5, -1],
store_path=os.path.join(
dest_case_folder,
'MergeRoi_Resize_05x05.nii'))
def Normalize():
root_folder = r'D:\ProblemData\Prcoessed'
for case in os.listdir(root_folder):
case_folder = os.path.join(root_folder, case)
if not os.path.isdir(case_folder):
continue
print('Normalizing PZ of {}'.format(case))
wg_path = os.path.join(case_folder, 'prostate_roi.nii.gz')
cg_path = os.path.join(case_folder, 'cg.nii')
if not os.path.exists(wg_path) and not os.path.exists(cg_path):
continue
wg_image, _, wg_data = LoadNiiData(wg_path, dtype=np.uint8)
cg_image, _, cg_data = LoadNiiData(cg_path, dtype=np.uint8)
print(np.unique(wg_data), np.unique(cg_data))
if list(np.unique(wg_data)) == [0, 255]:
wg = np.array(wg_data / 255, dtype=np.uint8)
else:
wg = wg_data
if list(np.unique(cg_data)) == [0, 255]:
cg = np.array(cg_data / 255, dtype=np.uint8)
else:
cg = cg_data
wg_array = GetImageFromArrayByImage(wg, wg_image)
cg_array = GetImageFromArrayByImage(cg, wg_image)
SaveNiiImage(os.path.join(case_folder, 'normalize_prostate_roi.nii'),
wg_array)
SaveNiiImage(os.path.join(case_folder, 'normalize_cg.nii'), cg_array)
print(np.unique(wg), np.unique(cg))
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 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 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 CheckROIForSeries(root_folder, store_folder, key, roi_key):
import glob
from scipy.misc import imsave
from MeDIT.SaveAndLoad import LoadNiiData, SaveArrayAsGreyImage
for case in os.listdir(root_folder):
case_folder = os.path.join(root_folder, case)
if not os.path.isdir(case_folder):
continue
print(case)
key_path = glob.glob(os.path.join(case_folder, key))
if len(key_path) != 1:
print('More Key Image: ', case)
continue
image, _, data = LoadNiiData(key_path[0])
show_data = FlattenAllSlices(data, is_show=False)
show_roi_list = []
roi_path_list = glob.glob(os.path.join(case_folder, roi_key))
if len(roi_path_list) == 0:
print('No ROI Image: ', case)
continue
for roi_path in roi_path_list:
_, _, roi = LoadNiiData(roi_path, dtype=np.uint8)
show_roi = FlattenAllSlices(roi, is_show=False)
if show_data.shape != show_roi.shape:
print('Data and ROI are not consistent: ', case)
show_roi_list.append(show_roi)
merge_data = MergeImageWithROI(show_data, show_roi_list)
store_path = os.path.join(store_folder, case + '.jpg')
imsave(store_path, merge_data)
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 main():
# PredictNii()
t2_folder = test_folder
predict_folder = r'Y:\142\zhangyihong\Documents\TZ_ROI\DSN_Add65_3Input\input3_output0_down\prediction'
t2_list = os.listdir(t2_folder)
cg_dice_list = []
pz_dice_list = []
for case in t2_list:
case_path = os.path.join(t2_folder, case)
predict_path = os.path.join(predict_folder, case)
t2_path = os.path.join(case_path, 't2.nii')
merge_roi_path = os.path.join(case_path, 'merge_pz1_cg2.nii')
predict_roi_path = os.path.join(predict_path, 'prediction.nii')
t2_image, _, t2_data = LoadNiiData(t2_path,
dtype=np.float32,
is_show_info=False)
_, _, roi_data = LoadNiiData(merge_roi_path,
dtype=np.uint8,
is_show_info=False)
_, _, predict_roi_data = LoadNiiData(predict_roi_path,
dtype=np.uint8,
is_show_info=False)
cg_dice_list.append(
Dice((roi_data == 2).astype(int),
(predict_roi_data == 2).astype(int)))
pz_dice_list.append(
Dice((roi_data == 1).astype(int),
(predict_roi_data == 1).astype(int)))
print(
'cg dice:',
Dice((roi_data == 2).astype(int),
(predict_roi_data == 2).astype(int)))
print(
'pz dice:',
Dice((roi_data == 1).astype(int),
(predict_roi_data == 1).astype(int)))
# from MeDIT.Visualization import Imshow3DArray
# from MeDIT.Normalize import Normalize01
#
# Imshow3DArray(Normalize01(t2_data), roi=(roi_data == 1).astype(int))
# Imshow3DArray(Normalize01(t2_data), roi=[(predict_roi_data == 1).astype(int), (roi_data == 1).astype(int)])
ShowHist(cg_dice_list,
save_path=os.path.join(model_path, 'cg_dice_hist.jpg'),
title='dice of cg')
ShowHist(pz_dice_list,
save_path=os.path.join(model_path, 'pz_dice_hist.jpg'),
title='dice of pz')
print('mean dice of cg:', sum(cg_dice_list) / len(cg_dice_list))
print('mean dice of pz:', sum(pz_dice_list) / len(pz_dice_list))
def main():
# LoadPath
data_folder = r'H:/data/TZ roi/data/Train'
file_list = os.listdir(data_folder)
t2PZ_save_path = r'H:/data/TZ roi/data/Train/t2_CG'
t2_path = os.path.join(data_folder, file_list[25] + '/t2.nii')
t2CG_path = os.path.join(data_folder, file_list[25] + '/t2_roi_hy.nii')
t2WG_path = os.path.join(data_folder, file_list[25] + '/prostate_roi_TrumpetNet.nii')
t2PZ_path = os.path.join(t2PZ_save_path, 't2_PZ_roi.nii.gz')
t2_image, _, t2 = LoadNiiData(t2_path, dtype=np.float32)
t2CG_image, _, t2CG_roi = LoadNiiData(t2CG_path, dtype=np.uint8)
t2WG_image, _, t2WG_roi = LoadNiiData(t2WG_path, dtype=np.uint8)
Roi_array = GetPZ(t2CG_roi, t2WG_roi)
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 TestNormalize():
# cg
cg_path = r'X:\RawData\TZ_ROI_20191119\prostate segmentation _PZ_TZ\Nii\2019-CA-formal-QIAN SHENG KUN\ROI_jkw0.nii'
# wg
wg_path = r'X:\RawData\TZ_ROI_20191119\prostate segmentation _PZ_TZ\Nii\2019-CA-formal-QIAN SHENG KUN\ROI_jkw1.nii'
t2_path = r'X:\RawData\TZ_ROI_20191119\prostate segmentation _PZ_TZ\Nii\2019-CA-formal-QIAN SHENG KUN\t2_Resize.nii'
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
_, _, t2 = LoadNiiData(t2_path)
_, _, cg = LoadNiiData(cg_path, dtype=np.uint8)
_, _, wg = LoadNiiData(wg_path, dtype=np.uint8)
Imshow3DArray(Normalize01(t2), roi=[Normalize01(cg), Normalize01(wg)])
def NiiImageInfoStatistic(root_folder, key_word):
case_list = os.listdir(root_folder)
case_list.sort()
shape_list = []
spacing_list = []
for case in case_list:
case_folder = os.path.join(root_folder, case)
candidate_file = glob.glob(os.path.join(case_folder, key_word))
if len(candidate_file) != 1:
print('Not unique file: ', case)
continue
image, _, data = LoadNiiData(candidate_file[0])
shape_list.append(image.GetSize())
spacing_list.append(image.GetSpacing())
shape_info = np.array(shape_list)
spacing_info = np.array(spacing_list)
print('The number of cases is :', len(shape_list))
print('The mean of the size is :', np.mean(shape_info, axis=0))
print('The max of the size is :', np.max(shape_info, axis=0))
print('The min of the size is :', np.min(shape_info, axis=0))
print('The mean of the spacing is :', np.mean(spacing_info, axis=0))
print('The max of the spacing is :', np.max(spacing_info, axis=0))
print('The min of the spacing is :', np.min(spacing_info, axis=0))
import matplotlib.pyplot as plt
fig = plt.figure(0, [15, 5])
ax1 = fig.add_subplot(131)
ax1.hist(spacing_info[:, 0], bins=50)
ax1.set_xlabel('Resolution')
ax1.set_ylabel('Spacing/mm')
ax1.set_title('Histogram of x-axis spacing')
ax2 = fig.add_subplot(132)
ax2.hist(spacing_info[:, 1], bins=50)
ax2.set_xlabel('Resolution')
ax2.set_ylabel('Spacing/mm')
ax2.set_title('Histogram of y-axis spacing')
ax3 = fig.add_subplot(133)
ax3.hist(spacing_info[:, 2], bins=50)
ax3.set_xlabel('Resolution')
ax3.set_ylabel('Spacing/mm')
ax3.set_title('Histogram of z-axis spacing')
plt.show()
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 TestGetPzRegion():
# pz_path = r'X:\PrcoessedData\PzTzSegment_ZYH\DoctorDraw2\2019-CA-formal-JIANG HONG GEN\pz.nii'
t2_path = r'D:\ProblemData\Prcoessed\Chen xian ming\t2.nii'
cg_path = r'D:\ProblemData\Prcoessed\Chen xian ming\cg.nii'
wg_path = r'D:\ProblemData\Prcoessed\Chen xian ming\prostate_roi.nii.gz'
from MeDIT.Visualization import Imshow3DArray
from MeDIT.Normalize import Normalize01
import matplotlib.pyplot as plt
_, _, t2 = LoadNiiData(t2_path)
# _, _, pz = LoadNiiData(pz_path, dtype=np.uint8)
_, _, cg = LoadNiiData(cg_path, dtype=np.uint8)
_, _, wg = LoadNiiData(wg_path, dtype=np.uint8)
Imshow3DArray(Normalize01(t2), roi=[wg, cg])
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 ShowOne():
path = r'X:\PrcoessedData\ProstateCancerECE\FCX^fang chun xiang\t2.nii'
roi0_path = r'X:\PrcoessedData\ProstateCancerECE\FCX^fang chun xiang\roi0.nii'
roi1_path = r'X:\PrcoessedData\ProstateCancerECE\FCX^fang chun xiang\roi1.nii'
# roi2_path = r'X:\PrcoessedData\ProstateCancerECE\CDM^chen di ming\roi2.nii'
_, data, _ = LoadNiiData(path)
_, roi0, _ = LoadNiiData(roi0_path)
_, roi1, _ = LoadNiiData(roi1_path)
# _, roi2, _ = LoadNiiData(roi2_path)
data = data.transpose((1, 2, 0))
roi0 = roi0.transpose((1, 2, 0))
roi1 = roi1.transpose((1, 2, 0))
# roi2 = roi2.transpose((1, 2, 0))
Imshow3DArray(Normalize01(data), roi=[roi0, roi1])
def SelectThree(PIRADS, ECE, roi0_path, roi1_path, roi2_path):
if ECE[0] == ECE[1] == ECE[2]:
if PIRADS[0] == PIRADS[1] == PIRADS[2]:
_, data0, _ = LoadNiiData(roi0_path)
_, data1, _ = LoadNiiData(roi1_path)
_, data2, _ = LoadNiiData(roi2_path)
volumn = [np.sum(data0), np.sum(data1), np.sum(data2)]
roi = [volumn.index(max(volumn[0], volumn[1], volumn[2]))]
# print(case, 'VolumnMax', roi)
else:
roi = GetPiradsMax(PIRADS)
# print(case, 'MaxPIRADS', roi)
else:
roi = GetECETrue(ECE)
# print(case, 'ECETrue', roi)
return roi
def AugmentScript():
from MeDIT.SaveAndLoad import LoadNiiData
from MeDIT.Visualization import Imshow3DArray, DrawBoundaryOfBinaryMask
from MeDIT.Normalize import Normalize01
import numpy as np
import time
t2_image, _, t2 = LoadNiiData(r'H:/data/TZ roi/BIAN ZHONG BEN/t2.nii',
dtype=np.float32)
roi_image, _, roi = LoadNiiData(
r'H:/data/TZ roi/BIAN ZHONG BEN/prostate_roi_TrumpetNet.nii',
dtype=np.uint8)
Imshow3DArray(t2, ROI=roi)
t2_slice = t2[..., 10]
roi_slice = roi[..., 10]
# DrawBoundaryOfBinaryMask(t2_slice, roi_slice)
import matplotlib.pyplot as plt
# plt.imshow(np.concatenate((Normalize01(t2_slice), Normalize01(roi_slice)), axis=1), cmap='gray')
# plt.show()
from MeDIT.DataAugmentor import DataAugmentor2D, AugmentParametersGenerator
param_dict = {
'stretch_x': 0.1,
'stretch_y': 0.1,
'shear': 0.1,
'rotate_z_angle': 20,
'horizontal_flip': True
}
augment_generator = AugmentParametersGenerator()
augmentor = DataAugmentor2D()
while True:
augment_generator.RandomParameters(param_dict)
transform_param = augment_generator.GetRandomParametersDict()
print(transform_param)
augment_t2 = augmentor.Execute(t2_slice,
aug_parameter=transform_param,
interpolation_method='linear')
augment_roi = augmentor.Execute(roi_slice,
aug_parameter=transform_param,
interpolation_method='linear')
def SelectTwo(PIRADS, ECE, roi0_path, roi1_path):
if ECE[0] != ECE[1]:
roi = GetECETrue(ECE)
# print(case, 'ECE=True:', roi)
else:
if PIRADS[0] != PIRADS[1]:
roi = GetPiradsMax(PIRADS)
# print(case, 'PIRADSMAX', roi)
else:
_, data0, _ = LoadNiiData(roi0_path)
_, data1, _ = LoadNiiData(roi1_path)
volumn0 = np.sum(data0)
volumn1 = np.sum(data1)
if volumn0 > volumn1:
roi = [0]
else:
roi = [1]
# print(case, 'VolumnMax', roi)
return roi
def show_feature_map(self, show_img_path, show_roi_path,
show_feature_map_path, store_path):
from MeDIT.SaveAndLoad import LoadNiiData
_, _, feature_map_array = LoadNiiData(show_feature_map_path)
featuremapvisualization = FeatureMapVisualizition()
featuremapvisualization.LoadData(show_img_path, show_roi_path,
show_feature_map_path)
# hsv/jet/gist_rainbow
featuremapvisualization.Show(color_map='rainbow',
store_path=store_path)
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 test():
t2_path = r'X:\PrcoessedData\ProstateCancerECE\CHEN ZHENG\t2.nii'
roi0_path = r'X:\PrcoessedData\ProstateCancerECE\CHEN ZHENG\roi.nii'
# roi1_path = r'X:\PrcoessedData\ProstateCancerECE\SXC^su xue cao\roi1.nii'
# roi2_path = r'X:\PrcoessedData\ProstateCancerECE\SXC^su xue cao\roi.nii'
_, _, t2 = LoadNiiData(t2_path)
_, _, roi0 = LoadNiiData(roi0_path)
# _, roi1, _ = LoadNiiData(roi1_path)
# _, roi2, _ = LoadNiiData(roi2_path)
label_im0, nb_labels0 = ndimage.label(roi0)
# label_im1, nb_labels1 = ndimage.label(roi1)
# label_im2, nb_labels2 = ndimage.label(roi2)
# roi0_list = []
# roi1_list = []
# roi2_list = []
# for index in range(1, nb_labels0 + 1):
# num = str(label_im0.tolist()).count(str(index))
# roi0_list.append(num)
new_mask1 = np.zeros(roi0.shape)
new_mask2 = np.zeros(roi0.shape)
# new_mask3 = np.zeros(roi0.shape)
new_mask1[label_im0 == 1] = 1
new_mask2[label_im0 == 2] = 1
# new_mask3[label_im0 == 3] = 1
# for index in range(1, nb_labels1 + 1):
# num = str(label_im1.tolist()).count(str(index))
# roi1_list.append(num)
# for index in range(1, nb_labels2 + 1):
# num = str(label_im2.tolist()).count(str(index))
# roi2_list.append(num)
# volumn = [np.sum(data0), np.sum(data1), np.sum(data2)]
# roi = volumn.index(max(volumn[0], volumn[1], volumn[2]))
# print(roi0_list, roi1_list, roi2_list)
Imshow3DArray(Normalize01(t2),
roi=[Normalize01(new_mask1),
Normalize01(new_mask2)])
def GetPzRegion():
root_folder = r'D:\ProblemData\Prcoessed'
for case in os.listdir(root_folder):
case_folder = os.path.join(root_folder, case)
if not os.path.isdir(case_folder):
continue
print('Estimating PZ of {}'.format(case))
wg_path = os.path.join(case_folder, 'prostate_roi.nii.gz')
cg_path = os.path.join(case_folder, 'cg.nii')
t2_path = os.path.join(case_folder, 't2.nii')
if not os.path.exists(wg_path) and not os.path.exists(cg_path):
continue
wg_image, _, wg_data = LoadNiiData(wg_path, dtype=np.uint8)
cg_image, _, cg_data = LoadNiiData(cg_path, dtype=np.uint8)
# # Calculating PZ
pz_data = wg_data - cg_data
# print(np.unique(pz_data))
pz_data[pz_data == 255] = 0
# from MeDIT.Visualization import Imshow3DArray
# from MeDIT.Normalize import Normalize01
#
# _, _, t2 = LoadNiiData(t2_path)
#
# Imshow3DArray(np.concatenate([Normalize01(t2), Normalize01(t2)], axis=1), roi=np.concatenate([pz_data0, pz_data], axis=1))
#
# print(np.unique(pz_data), np.unique(wg_data), np.unique(cg_data))
# Save PZ image
pz_image = GetImageFromArrayByImage(pz_data, wg_image)
SaveNiiImage(os.path.join(case_folder, 'pz.nii'), pz_image)
def main():
pass
# random_params = {'stretch_x': 0.1, 'stretch_y': 0.1, 'shear': 0.1, 'rotate_z_angle': 20, 'horizontal_flip': True}
# param_generator = AugmentParametersGenerator()
# aug_generator = DataAugmentor2D()
# from MeDIT.SaveAndLoad import LoadNiiData
# _, _, data = LoadNiiData(data_path)
# _, _, roi = LoadNiiData(roi_path)
# data = data[..., data.shape[2] // 2]
# roi = roi[..., roi.shape[2] // 2]
# from Visualization import DrawBoundaryOfBinaryMask
# from Normalize import Normalize01
# while True:
# param_generator.RandomParameters(random_params)
# aug_generator.SetParameter(param_generator.GetRandomParametersDict())
#
# new_data = aug_generator.Execute(data, interpolation_method='linear')
# new_roi = aug_generator.Execute(roi, interpolation_method='nearest')
# DrawBoundaryOfBinaryMask(Normalize01(new_data), new_roi)
# Test ElasticAugment
from MeDIT.SaveAndLoad import LoadNiiData
from MeDIT.Visualization import Imshow3DArray
import matplotlib.pyplot as plt
image, _, data = LoadNiiData(
r'C:\Users\yangs\Desktop\QIAN XING CHUN_siemens\QIAN XING CHUN\MR\20190101\140102.198000\MR20190101140046\005_t2_tse_tra_384_p2.nii'
)
# data = data[..., data.shape[-1] // 2]
# plt.imshow(data, cmap='gray')
# plt.show()
while True:
aug_data = ElasticAugment(
data,
num_controlpoints=4,
std_deformation_sigma=3,
proportion_to_augment=0.5,
)
# plt.imshow(np.concatenate((data, aug_data, np.abs(data - aug_data)), axis=1), cmap='gray')
# plt.show()
Imshow3DArray(aug_data[0] - data)
def test(resample_folder):
case_list = os.listdir(resample_folder)
row_list = []
channel_list = []
for case in case_list:
case_path = os.path.join(resample_folder, case)
roi_path = os.path.join(case_path, 'roi.nii')
if not os.path.exists(roi_path):
print('% has no roi'.format(case))
continue
_, roi, _ = LoadNiiData(roi_path)
slice = SelectMaxRoiSlice(roi)
# max_row, max_channel = GetRoiSize(roi[slice, ...])
size = GetRoiSize(roi[slice, ...])
if size[0] >= 100 or size[1] >= 100:
print(case, ':', size)
def PredictNii():
model_folder_path = model_path
t2_folder = test_folder
store_folder = r'D:\test'
t2_list = os.listdir(t2_folder)
prostate_segmentor = ProstatePzCgSegmentationInput_3()
prostate_segmentor.LoadConfigAndModel(model_folder_path)
for case in t2_list:
print('Predicting ', case)
case_path = os.path.join(t2_folder, case)
store_path = os.path.join(store_folder, case)
if not os.path.exists(store_path):
os.mkdir(store_path)
t2_path = os.path.join(case_path, 't2.nii')
t2_image, _, t2_data = LoadNiiData(t2_path,
dtype=np.float32,
is_show_info=False)
prostate_segmentor.Run(t2_image,
store_folder=os.path.join(
store_path, 'prediction.nii'))
def GetDWIPath(case):
print(case)
if case == 'WYB^wu yi bao' or case == 'ZYB^zhang yun bao':
return 0
b_value = ['0', '50', '700', '750', '1400', '1500']
case_path = os.path.join(process_folder, case)
if os.path.exists(os.path.join(case_path, 'adc.nii')):
if os.path.exists(os.path.join(case_path, 'dwi.nii')):
bval_path = os.path.join(case_path, 'dwi.bval')
bval = open(bval_path, 'r')
b_list = bval.read().split()
if len(b_list) == 1:
new_img, new_dwi, _ = LoadNiiData(os.path.join(case_path, 'dwi.nii'))
else:
b, index = NearTrueB(b_list)
_, dwi, _ = LoadNiiData(os.path.join(case_path, 'dwi.nii'))
img, _, _ = LoadNiiData(os.path.join(case_path, 'adc.nii'))
new_dwi = dwi[index, ...]
new_img = sitk.GetImageFromArray(new_dwi)
new_img.SetDirection(img.GetDirection())
new_img.SetSpacing(img.GetSpacing())
new_img.SetOrigin(img.GetOrigin())
elif os.path.exists(os.path.join(case_path, 'dki.nii')):
bval_path = os.path.join(case_path, 'dki.bval')
bval = open(bval_path, 'r')
b_list = bval.read().split()