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croppedSeg3DkerasDR_gh.py
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croppedSeg3DkerasDR_gh.py
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def croppedSeg3DkerasDR(trainMode,testMode,params):
print('21!')
import os, sys
sys.path.insert(0, '/add-directory-path-where-needed/this-folder')
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import funcs_gh
import matplotlib.pyplot as plt
import pickle
from datetime import datetime, timedelta
import numpy as np
from keras.models import Model,load_model,Sequential
from keras.layers import Reshape,Input, concatenate, Conv3D,Dense,TimeDistributed
from keras.layers import MaxPooling3D, UpSampling3D, LSTM,ConvLSTM2D
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint,TensorBoard
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot
import tensorflow as tf
from networks_gh import get_unet2, get_unet3, get_rbunet, get_meshNet, get_denseNet, calculatedPerfMeasures
from networks_gh import get_unetCnnRnn
from networks_gh import get_denseNet103, augment_sample_segment
from selectTrainAndTestSubjects_gh import selectTrainAndTestSubjects
from scipy.ndimage import zoom
from scipy import signal
from skimage import morphology
from skimage import data
from skimage.feature import corner_harris, corner_subpix, corner_peaks
from skimage.transform import warp, AffineTransform
from sklearn.decomposition import PCA, KernelPCA
from scipy.interpolate import interp1d
import pandas as pd
TestSetNum=params['TestSetNum'];
fileNumModel=params['fileNumModel'];
tDim=params['tDim'];
tpUsed=params['tpUsed'];
PcUsed=params['PcUsed'];
deepRed=params['deepReduction'];
if PcUsed:
tDim=5;
xDim=64; yDim=64; zDim=64;
xyDim=64;
n_channels = tDim;
n_classes = 2 # total classes (kidney, non-kidney)
if PcUsed==1:
pc='pc';
elif PcUsed==2:
pc='kpc/';
elif PcUsed==3:
pc='tsne/';
elif PcUsed==4:
pc='/'
net=params['networkToUse'];
if net=='meshNet':
xyDim=96;zDim=96;
############ stratify train and test data #########
subjectNamesNormalTrain, subjectNamesNormalTest, _ ,testKidCond ,subjectBaselinesTest = selectTrainAndTestSubjects(TestSetNum);
############ generate train batch data #########
def generate_batch():
for samples in generate_samples():
label_batch=np.zeros((len(samples)*2,zDim,xyDim,xyDim,2))
image_batch=np.zeros((len(samples)*2,zDim,xyDim,xyDim,tDim))
for s in range(len(samples)):
data4D = pickle.load(open("/path-to-folder-containing-downsampled-images-for-segmentation-model/singleSubjectsCroppedV4pc_segment/"+subjectNamesNormalTrain[samples[s]]+'_tp'+str(tpUsed)+".p","rb" ));
labels=data4D[subjectNamesNormalTrain[samples[s]]+'M'];
labels[labels>1]=1;labels[labels<1]=0;
labels=labels[:,:,:,:,np.newaxis];labels=np.concatenate((labels,1-labels),axis=4);
da = data4D[subjectNamesNormalTrain[samples[s]]+'D'];
if net=='meshNet':
n=16;
da2=np.pad(da,((0,0),(n,n), (n, n),(n,n),(0,0)), 'edge');
labels2=np.pad(labels,((0,0),(n,n),(n,n),(n,n),(0,0)), 'edge');
image_batch[2*s:2*s+2,:,:,:,:]= da;
label_batch[2*s:2*s+2,:,:,:,:]= labels;
# generate data augmentation
#for ix in range(image_batch.shape[0]):
# image_batch[ix], label_batch[ix] = augment_sample_segment(image_batch[ix],label_batch[ix]); #,[2,5],10)
yield(image_batch, label_batch)
n_samples = 46; # number of training image files
batch_size = 3 # size of batch
n_batches = int(n_samples/batch_size); # number of batches
def generate_samples():
sample_ids = np.random.permutation(n_samples)
for i in range(n_batches):
inds = slice(i*batch_size, (i+1)*batch_size)
yield sample_ids[inds]
################### visual check for cropped segmentaation
#for i in range(40,42):
# # plt.figure();plt.imshow(Data[i,:,:,10,0].T);
# f, axarr = plt.subplots(1, 2);
#
# # axarr[0].imshow(DataTest[0,zFix,:,:,1].T);
# axarr[0].imshow(Data[i,:,:,40,2].T);
# axarr[1].imshow(Labels[i,:,:,40,0].T);
### dice accuracy
def dice_coef(y_true, y_pred):
y_true_f = y_true.flatten();
y_pred_f = y_pred.flatten()
intersection = np.sum(y_true_f * y_pred_f)
return (2. * intersection) / (np.sum(y_true_f) + np.sum(y_pred_f));
### tversky_coefficient
def tversky_coef(y_true, y_pred, alpha, beta, smooth=1):
y_true_f = K.flatten(y_true)
y_true_f_r = K.flatten(1. - y_true)
y_pred_f = K.flatten(y_pred)
y_pred_f_r = K.flatten(1. - y_pred)
weights = 1.
intersection = K.sum(y_pred_f * y_true_f * weights)
fp = K.sum(y_pred_f * y_true_f_r)
fn = K.sum(y_pred_f_r * y_true_f * weights)
return (intersection + smooth) / (intersection + alpha * fp + beta * fn + smooth)
### tversky_coefficient loss
def tversky_loss(alpha, beta, weights=False):
def tversky(y_true, y_pred):
return -tversky_coef(y_true, y_pred, alpha, beta, weights)
return tversky
tversky = tversky_loss(alpha=0.3, beta=0.7, weights=False)
### initial class weights
class_weights=np.array([0.5,0.5]);
#class_weights=np.array([0.2,0.8]);
#class_weights=np.array([0.4,0.6]);
if net=='rbUnet':
model = get_rbunet(xyDim,zDim,n_channels,n_classes,deepRed,0);
elif net=='Unet':
model = get_unet3(xyDim,zDim,n_channels,n_classes,deepRed,0);
elif net=='Unet-rnn':
predLayersCat=get_unetCnnRnn(xyDim,zDim,1,n_classes,deepRed,0);
predLayersCat=Reshape((1,64, 64, 64))(predLayersCat)
for i in range(1,n_channels):
predLayer = get_unetCnnRnn(xyDim,zDim,1,n_classes,deepRed,0);
predLayer=Reshape((1,64, 64, 64))(predLayer)
predLayersCat= concatenate([predLayersCat, predLayer], axis=1)
model = Sequential()
model.add(ConvLSTM2D(64, kernel_size=3, padding='same',input_shape=(50, 64, 64,64)))
#lstm_num_predictions=1;
#model.add(Dense(lstm_num_predictions))
model = Model(inputs=[inputs], outputs=[Pred])
model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss2)
elif net=='meshNet':
xyDim=96;zDim=96;
model = get_meshNet(xyDim,zDim,n_channels,n_classes,deepRed,0);
elif net== 'denseNet':
model = get_denseNet(xyDim,zDim,n_channels,n_classes,deepRed,0);
elif net== 'tNet':
model = get_denseNet103(xyDim,zDim,n_channels,n_classes,deepRed,0);
### create folder to hold trained segmentation model(s)
if isinstance(fileNumModel, (int)):
fileNumModel=str(fileNumModel);
else:
fileNumModel='Net'+net+'_time'+str(tDim)+'_pcUsed'+str(PcUsed)+'_tpUsed'+str(tpUsed)+'_DR'+str(deepRed)+'_testSet'+str(TestSetNum);
address = "path-to-folder-to-hold-segmentation-model(s)/"+fileNumModel+"/"
if trainMode:
os.system('mkdir '+address);
#current_time = datetime.now() + timedelta(hours=-5)
#log_dir=address+str(current_time)[:19]
#callbacks = [
#TensorBoard(address+'tbevents',histogram_freq=0, write_graph=True, write_images=False),
#ModelCheckpoint(address+'conv3Dkeras.h5',verbose=1,monitor='val_loss', save_best_only=True, save_weights_only=True),
#]
nb_epoch = 400; #800
epCounter = 0;
for e in range(nb_epoch):
print("epoch %d" % e)
for image_batch, label_batch in generate_batch():
print(epCounter,label_batch.shape[0]);
xx=(batch_size*2)+1;
epCounter+=xx;
model.fit(image_batch, label_batch, batch_size=batch_size*2, class_weight=class_weights,
initial_epoch =epCounter, epochs=epCounter+xx,verbose=1, shuffle=True,validation_split=0.5); #,callbacks=callbacks);
model.save(address+'croppedSeg3D_'+str(epCounter)+'.h5')
"""
#save model between defined epCounter
if epCounter >= 500 and epCounter <= 1000:
model.save(address+'croppedSeg3D_'+str(epCounter)+'.h5')
if epCounter >= 5000 and epCounter <= 10000:
model.save(address+'croppedSeg3D_'+str(epCounter)+'.h5')
if epCounter >= 15000 and epCounter <= 20000:
model.save(address+'croppedSeg3D_'+str(epCounter)+'.h5')
if epCounter >= 28000 and epCounter <= 35000:
model.save(address+'croppedSeg3D_'+str(epCounter)+'.h5')
if epCounter >= 40000 and epCounter <= 45000:
model.save(address+'croppedSeg3D_'+str(epCounter)+'.h5')
if epCounter >= 60000:
model.save(address+'croppedSeg3D_'+str(epCounter)+'.h5')
"""
# # list all data in history
# print(history.history.keys())
# # summarize history for accuracy
# plt.plot(history.history['acc'])
# plt.plot(history.history['val_acc'])
# plt.title('model accuracy')
# plt.ylabel('accuracy')
# plt.xlabel('epoch')
# plt.legend(['train', 'val'], loc='upper left')
# plt.show()
# # summarize history for loss
# plt.plot(history.history['loss'])
# plt.plot(history.history['val_loss'])
# plt.title('model loss')
# plt.ylabel('loss')
# plt.xlabel('epoch')
# plt.legend(['train', 'val'], loc='upper left')
# plt.show()
if testMode == 1:
performanceMeasuresX,volumEstimError,performanceMeasures,avgPerf = [],[],[],[];
fileNumModel='Net'+net+'_time'+str(tDim)+'_pcUsed'+str(PcUsed)+'_tpUsed'+str(tpUsed)+'_DR'+str(deepRed)+'_testSet'+str(TestSetNum);
address = "path-to-folder-containing-trained-segmentation-model(s)/"+fileNumModel+"/"
#address = "path-to-folder-to-hold-segmentation-model(s)" + "/NettNet_time5_pcUsed1_tpUsed50_DR0_testSet1/"
#address = "path-to-folder-to-hold-segmentation-model(s)" + "/NettNet_time5_pcUsed1_tpUsed50_DR0_testSet2/"
selectedEpoch=params['selectedEpoch'];
# if isinstance(selectedEpoch, (int)):
# selectedEpoch=str(selectedEpoch);
# else:
# txt_file = open(address+'selectedEpoc.txt','r')
# selectedEpoch=str(int(txt_file.read()))
#
model.load_weights(address+'croppedSeg3D_'+selectedEpoch+'.h5');
# perform segmentation for each test subject using ground-truth detection 'box'
# to check the segmentation model's performance
dx=64; dy=64; dz=64; n_channels=5; #50
for s in range(len(subjectNamesNormalTest)):
pName = subjectNamesNormalTest[s];
DataCroppedTest=np.zeros((2,dx,dy,dz,n_channels));
# extract test image volume
sc=0;
data4D = pickle.load(open("/path-to-folder-containing-downsampled-images-for-segmentation-model"+"/singleSubjectsCroppedV4pc_segment/"+pName+'_tp'+str(tpUsed)+".p","rb"));
da=data4D[pName+'D'];
DataCroppedTest[2*sc:2*sc+2,:,:,:,:]=da;
# perform prediction using trained segmentation model
cropped_mask_test = model.predict(DataCroppedTest, verbose=1)
if cropped_mask_test.min()<0:
cropped_mask_test=abs(cropped_mask_test.min())+cropped_mask_test;
# extract predicted labels
imgs_mask_test2=np.copy(cropped_mask_test);
imgs_mask_test2[:,:,:,:,0]=cropped_mask_test[:,:,:,:,0];
imgs_mask_test2[:,:,:,:,1]=cropped_mask_test[:,:,:,:,1];
labels_pred_2=np.argmax(imgs_mask_test2, axis=4);
# path to .xls sheet that contains time information for each test subject file (pName)
fileAddress='path-to-folder"+"/subjectDicomInfo_gh.xls';
subjectInfo=pd.read_excel(fileAddress, sheetname=0);
reconMethod='SCAN';
# extract ground-truth kidney mask (KM) and bounding box for each kidney (Box)
vol4D00, KM, Box, _, _ = funcs_gh.readData4(pName,subjectInfo,reconMethod,1);
KM[KM>1]=1;
zDimOrig = vol4D00.shape[2];
Box=np.reshape(Box,[2,6]).astype('int');
# identify whether right kidney exists
# identify whether left kidney exists
kidneyNone=np.nonzero(np.sum(Box,axis=1)==0); #right/left
if kidneyNone[0].size!=0:
kidneyNone=np.nonzero(np.sum(Box,axis=1)==0)[0][0]; #right/left
# add extra margins
xSafeMagin=10;ySafeMagin=10;zSafeMagin=3;
if Box[0,2]+Box[0,5]+3 >= KM.shape[2] or Box[0,2]+Box[0,5]-3 <0:
Box[:,[3,4,5]]=Box[:,[3,4,5]]+[xSafeMagin,ySafeMagin,0];
else:
Box[:,[3,4,5]]=Box[:,[3,4,5]]+[xSafeMagin,ySafeMagin,zSafeMagin];
# add extra margins
# xSafeMagin=12;ySafeMagin=12;zSafeMagin=3;
# Box[:,[3,4,5]]=Box[:,[3,4,5]]+[xSafeMagin,ySafeMagin,zSafeMagin];
# resample predicted kidney labels to appropriate size and location
# of original test subject dimensions
xyDim=224;
predMaskR=np.zeros((1,xyDim,xyDim,zDimOrig));
predMaskL=np.zeros((1,xyDim,xyDim,zDimOrig));
if kidneyNone!=0:
Rk=labels_pred_2[2*sc,:,:,:]
croppedData4DR=signal.resample(Rk,Box[0,3], t=None, axis=0);
croppedData4DR=signal.resample(croppedData4DR,Box[0,4], t=None, axis=1);
croppedData4DR=signal.resample(croppedData4DR,Box[0,5], t=None, axis=2);
croppedData4DR[croppedData4DR>0.5]=2;croppedData4DR[croppedData4DR<0.5]=0
croppedData4DR[croppedData4DR==0]=1;croppedData4DR[croppedData4DR==2]=0
predMaskR[sc,int(Box[0,0]-Box[0,3]/2):int(Box[0,0]+Box[0,3]/2),\
int(Box[0,1]-Box[0,4]/2):int(Box[0,1]+Box[0,4]/2),\
int(Box[0,2]-Box[0,5]/2):int(Box[0,2]+Box[0,5]/2)]=croppedData4DR;
if kidneyNone!=1:
Lk=labels_pred_2[2*sc+1,:,:,:]
croppedData4DL=signal.resample(Lk,Box[1,3], t=None, axis=0);
croppedData4DL=signal.resample(croppedData4DL,Box[1,4], t=None, axis=1);
croppedData4DL=signal.resample(croppedData4DL,Box[1,5], t=None, axis=2);
croppedData4DL[croppedData4DL>0.5]=2; croppedData4DL[croppedData4DL<0.5]=0
croppedData4DL[croppedData4DL==0]=1;croppedData4DL[croppedData4DL==2]=0
predMaskL[sc,int(Box[1,0]-Box[1,3]/2):int(Box[1,0]+Box[1,3]/2),\
int(Box[1,1]-Box[1,4]/2):int(Box[1,1]+Box[1,4]/2),\
int(Box[1,2]-Box[1,5]/2):int(Box[1,2]+Box[1,5]/2)]=croppedData4DL;
if np.sum(predMaskR) != 0:
predMaskL=morphology.remove_small_objects(predMaskL.astype(bool), min_size=256,in_place=True).astype(int);
if np.sum(predMaskL) != 0:
predMaskR=morphology.remove_small_objects(predMaskR.astype(bool), min_size=256,in_place=True).astype(int);
predMaskL2=np.copy(predMaskL);
predMaskL2[predMaskL2==1]=2;
Masks2Save={};
predMaskR2=zoom(predMaskR[sc,:,:,:],(1,1,1),order=0);
predMaskL2=zoom(predMaskL[sc,:,:,:],(1,1,1),order=0);
Masks2Save['R']=np.copy(predMaskR2.astype(float));
Masks2Save['L']=np.copy(predMaskL2.astype(float));
print(pName)
pathToFolder = "path-to-folder-to-contain-segmented-image-files"+"/segmented/" + pName + '_seq1'
if not os.path.exists(pathToFolder):
os.makedirs(pathToFolder)
funcs_gh.writeMasks(pName,subjectInfo,reconMethod,Masks2Save,1);
"""
xDim=64; yDim=64; zDim=64; tDim=5;
LabelsTest2=np.zeros((len(subjectNamesNormalTest)*2,xDim,yDim,zDim))
DataTest2=np.zeros((len(subjectNamesNormalTest)*2,xDim,yDim,zDim,tDim))
#LabelsTest2=np.zeros((1*2,xDim,yDim,zDim))
#DataTest2=np.zeros((1*2,xDim,yDim,zDim,tDim))
# obtain input image data and ground-truth for total test data (cropped)
for s in range(len(subjectNamesNormalTest)):
data4D = pickle.load(open("/fileserver/abd/marzieh/preprocessedData_ha/singleSubjectsCroppedV4pc_segment/"+subjectNamesNormalTest[s]+'_tp'+str(tpUsed)+".p","rb" ));
la=data4D[subjectNamesNormalTest[s]+'M'];
LabelsTest2[2*s:2*s+2,:,:,:]=la;
da=data4D[subjectNamesNormalTest[s]+'D'];
DataTest2[2*s:2*s+2,:,:,:,:]=da;
if net=='meshNet':
n=16;
DataTest2=np.pad(DataTest2,((0,0),(n,n), (n, n),(n,n),(0,0)), 'edge');
LabelsTest2=np.pad(LabelsTest2,((0,0),(n,n), (n, n),(n,n)), 'edge');
# obtain prediction for total test data (cropped)
imgs_mask_test = model.predict(DataTest2, verbose=1)
if imgs_mask_test.min()<0:
imgs_mask_test=abs(imgs_mask_test.min())+imgs_mask_test
imgs_mask_test2=np.copy(imgs_mask_test);
imgs_mask_test2[:,:,:,:,0]=imgs_mask_test[:,:,:,:,0]
imgs_mask_test2[:,:,:,:,1]=imgs_mask_test[:,:,:,:,1]
labels_pred=np.argmax(imgs_mask_test2, axis=4);
labels_pred[labels_pred>1]=1;labels_pred[labels_pred<0]=0;
#import pandas as pd
columns = ['Name','kidney Condition','F1-Score', 'Prec','Rec','VEE','testSet','Model'];
index=np.arange(len(subjectNamesNormalTest));
performanceMeasures= pd.DataFrame(index=index, columns=columns)
performanceMeasures= performanceMeasures.fillna(0);
# compute spatial overlap performance of prediction for total test data (cropped)
for s in range(len(subjectNamesNormalTest)):
right=calculatedPerfMeasures(LabelsTest2[2*s,:,:,:],labels_pred[2*s,:,:,:]);
left=calculatedPerfMeasures(LabelsTest2[(2*s)+1,:,:,:],labels_pred[(2*s)+1,:,:,:]);
avgPerfOverKidneys=np.mean([right,left],axis=0);
performanceMeasures.ix[s]=pd.Series({'Name':subjectNamesNormalTest[s],'kidney Condition':testKidCond[s],'F1-Score':avgPerfOverKidneys[0]*100,'Prec':avgPerfOverKidneys[1]*100,
'Rec':avgPerfOverKidneys[2]*100,'VEE':avgPerfOverKidneys[4],'testSet':TestSetNum,'Model':net+'-DR'+str(deepRed)+'-PC'+str(PcUsed)});
normalPerf=performanceMeasures[performanceMeasures['kidney Condition'] == 'N'].iloc[:,2:6].mean().tolist();
abnormalPerf=performanceMeasures[performanceMeasures['kidney Condition'] == 'A'].iloc[:,2:6].mean().tolist();
avgPerf=np.round(normalPerf+abnormalPerf,2)
volumEstimError=np.zeros((labels_pred.shape[0],))
performanceMeasuresX=np.zeros((2*len(subjectNamesNormalTest),));
whatIs = labels_pred.shape[0]
for i in range(labels_pred.shape[0]):
# zAx=20;
# f, axarr = plt.subplots(1, 3);
# axarr[0].imshow(imgs_mask_test[0,:,:,0].T,cmap='gray');
# axarr[0].imshow(labels_pred[i,:,:,zAx].T,cmap='gray');axarr[0].set_title('prediction');
# axarr[1].imshow(LabelsTest2[i,:,:,zAx].T,cmap='gray');axarr[1].set_title('manual label');
# axarr[2].imshow(DataTest2[i,:,:,zAx,40].T,cmap='gray');
volumEstimError[i]= np.count_nonzero(LabelsTest2[i,:,:,:])-np.count_nonzero(labels_pred[i,:,:,:]);
performanceMeasuresX[i]= dice_coef(LabelsTest2[i,:,:,:],labels_pred[i,:,:,:])*100;
test = 1;
"""
return performanceMeasuresX,volumEstimError,performanceMeasures,avgPerf