def PredictModel():
model = settings.options.predictmodel
image = settings.options.predictimage
outdir = settings.options.segmentation
if (model != None and image != None and outdir != None):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
imagepredict = nib.load(image)
imageheader = imagepredict.header
numpypredict = imagepredict.get_data().astype(settings.IMG_DTYPE)
assert numpypredict.shape[0:2] == (settings._globalexpectedpixel,
settings._globalexpectedpixel)
nslice = numpypredict.shape[2]
resizepredict = skimage.transform.resize(
numpypredict, (settings.options.trainingresample,
settings.options.trainingresample, nslice),
order=0,
preserve_range=True,
mode='constant').astype(settings.IMG_DTYPE).transpose(2, 1, 0)
opt = GetOptimizer()
lss, met = GetLoss()
loaded_model = get_unet()
loaded_model.compile(loss=lss, metrics=met, optimizer=opt)
loaded_model.load_weights(model)
segout_float = loaded_model.predict(resizepredict[..., np.newaxis])
segout_int = (segout_float[..., 0] >=
settings.options.segthreshold).astype(settings.SEG_DTYPE)
segout_float_resize = skimage.transform.resize(
segout_float[..., 0], (nslice, settings._globalexpectedpixel,
settings._globalexpectedpixel),
order=0,
preserve_range=True,
mode='constant').transpose(2, 1, 0)
segout_float_img = nib.Nifti1Image(segout_float_resize,
None,
header=imageheader)
segout_float_img.to_filename(
outdir.replace('.nii.gz', '-predtumorfloat.nii.gz'))
segout_int_resize = skimage.transform.resize(
segout_int[..., 0], (nslice, settings._globalexpectedpixel,
settings._globalexpectedpixel),
order=0,
preserve_range=True,
mode='constant').transpose(2, 1, 0)
segout_int_img = nib.Nifti1Image(segout_int_resize,
None,
header=imageheader)
segout_int_img.to_filename(
outdir.replace('.nii.gz', '-predtumorseg.nii.gz'))
return segout_int_resize
def PredictModel(model=settings.options.predictmodel,
image=settings.options.predictimage,
imageheader=None,
outdir=settings.options.segmentation):
if (model != None and image != None and outdir != None):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
numpypredict, origheader, _ = preprocess.reorient(image)
assert numpypredict.shape[0:2] == (settings._globalexpectedpixel,
settings._globalexpectedpixel)
resizepredict = preprocess.resize_to_nn(numpypredict)
resizepredict = preprocess.window(resizepredict,
settings.options.hu_lb,
settings.options.hu_ub)
resizepredict = preprocess.rescale(resizepredict,
settings.options.hu_lb,
settings.options.hu_ub)
opt = GetOptimizer()
lss, met = GetLoss()
loaded_model = get_unet()
loaded_model.compile(loss=lss, metrics=met, optimizer=opt)
loaded_model.load_weights(model)
segout_float = loaded_model.predict(resizepredict[...,
np.newaxis])[..., 0]
segout_int = (segout_float >= settings.options.segthreshold).astype(
settings.SEG_DTYPE)
segin_windowed = preprocess.resize_to_original(resizepredict)
segin_windowed_img = nib.Nifti1Image(segin_windowed,
None,
header=origheader)
segin_windowed_img.to_filename(
outdir.replace('.nii', '-imgin-windowed.nii'))
segout_float_resize = preprocess.resize_to_original(segout_float)
segout_float_img = nib.Nifti1Image(segout_float_resize,
None,
header=origheader)
segout_float_img.to_filename(outdir.replace('.nii', '-pred-float.nii'))
segout_int_resize = preprocess.resize_to_original(segout_int)
segout_int_img = nib.Nifti1Image(segout_int_resize,
None,
header=origheader)
segout_int_img.to_filename(outdir.replace('.nii', '-pred-seg.nii'))
return segout_float_resize, segout_int_resize
def TrainModel(idfold=0):
from setupmodel import GetSetupKfolds, GetCallbacks, GetOptimizer, GetLoss
from buildmodel import get_unet, thick_slices
###
### load data
###
kfolds = settings.options.kfolds
print('loading memory map db for large dataset')
numpydatabase = np.load(settings._globalnpfile)
(train_index, test_index) = GetSetupKfolds(settings.options.dbfile, kfolds,
idfold)
print('copy data subsets into memory...')
axialbounds = numpydatabase['axialtumorbounds']
dataidarray = numpydatabase['dataid']
dbtrainindex = np.isin(dataidarray, train_index)
dbtestindex = np.isin(dataidarray, test_index)
subsetidx_train = np.all(np.vstack((axialbounds, dbtrainindex)), axis=0)
subsetidx_test = np.all(np.vstack((axialbounds, dbtestindex)), axis=0)
if np.sum(subsetidx_train) + np.sum(subsetidx_test) != min(
np.sum(axialbounds), np.sum(dbtrainindex)):
raise ("data error: slice numbers dont match")
print('copy memory map from disk to RAM...')
trainingsubset = numpydatabase[subsetidx_train]
np.random.seed(seed=0)
np.random.shuffle(trainingsubset)
totnslice = len(trainingsubset)
if settings.options.D3:
x_data = trainingsubset['imagedata']
y_data = trainingsubset['truthdata']
x_train = thick_slices(x_data, settings.options.thickness)
y_train = thick_slices(y_data, settings.options.thickness)
else:
x_train = trainingsubset['imagedata']
y_train = trainingsubset['truthdata']
slicesplit = int(0.9 * totnslice)
TRAINING_SLICES = slice(0, slicesplit)
VALIDATION_SLICES = slice(slicesplit, totnslice)
print("\nkfolds : ", kfolds)
print("idfold : ", idfold)
print("slices in kfold : ", totnslice)
print("slices training : ", slicesplit)
print("slices validation : ", totnslice - slicesplit)
try:
print("slices testing : ", len(numpydatabase[subsetidx_test]))
except:
print("slices testing : 0")
###
### data preprocessing : applying liver mask
###
y_train_typed = y_train.astype(settings.SEG_DTYPE)
liver_idx = y_train_typed > 0
y_train_liver = np.zeros_like(y_train_typed)
y_train_liver[liver_idx] = 1
tumor_idx = y_train_typed > 1
y_train_tumor = np.zeros_like(y_train_typed)
y_train_tumor[tumor_idx] = 1
x_masked = x_train * y_train_liver - 100.0 * (1.0 - y_train_liver)
x_masked = x_masked.astype(settings.IMG_DTYPE)
###
### set up output, logging, and callbacks
###
logfileoutputdir = '%s/%03d/%03d' % (settings.options.outdir, kfolds,
idfold)
os.system('mkdir -p ' + logfileoutputdir)
os.system('mkdir -p ' + logfileoutputdir + '/nii')
os.system('mkdir -p ' + logfileoutputdir + '/tumor')
print("Output to\t", logfileoutputdir)
###
### create and run model
###
opt = GetOptimizer()
callbacks, modelloc = GetCallbacks(logfileoutputdir, "tumor")
lss, met = GetLoss()
model = get_unet()
model.compile(loss=lss, metrics=met, optimizer=opt)
print("\n\n\tlivermask training...\tModel parameters: {0:,}".format(
model.count_params()))
if settings.options.augment:
train_datagen = ImageDataGenerator(
brightness_range=[0.95, 1.05],
width_shift_range=[-0.1, 0.1],
height_shift_range=[-0.1, 0.1],
horizontal_flip=True,
vertical_flip=True,
zoom_range=0.1,
fill_mode='nearest',
)
else:
train_datagen = ImageDataGenerator()
test_datagen = ImageDataGenerator()
if settings.options.D3:
train_generator = train_datagen.flow(
x_masked[TRAINING_SLICES, :, :, :, np.newaxis],
y_train_tumor[TRAINING_SLICES, :, :, :, np.newaxis],
batch_size=settings.options.trainingbatch)
test_generator = test_datagen.flow(
x_masked[TRAINING_SLICES, :, :, :, np.newaxis],
y_train_tumor[TRAINING_SLICES, :, :, :, np.newaxis],
batch_size=settings.options.validationbatch)
else:
train_generator = train_datagen.flow(
x_masked[TRAINING_SLICES, :, :, np.newaxis],
y_train_tumor[TRAINING_SLICES, :, :, np.newaxis],
batch_size=settings.options.trainingbatch)
test_generator = test_datagen.flow(
x_masked[VALIDATION_SLICES, :, :, np.newaxis],
y_train_tumor[VALIDATION_SLICES, :, :, np.newaxis],
batch_size=settings.options.validationbatch)
history_liver = model.fit_generator(
train_generator,
steps_per_epoch=slicesplit // settings.options.trainingbatch,
validation_steps=(totnslice - slicesplit) //
settings.options.validationbatch,
epochs=settings.options.numepochs,
validation_data=test_generator,
callbacks=callbacks)
###
### make predicions on validation set
###
print("\n\n\tapplying models...")
if settings.options.D3:
y_pred_float = model.predict(x_masked[VALIDATION_SLICES, :, :, :,
np.newaxis])
else:
y_pred_float = model.predict(x_masked[VALIDATION_SLICES, :, :,
np.newaxis])
y_pred_seg = (y_pred_float[..., 0] >=
settings.options.segthreshold).astype(settings.SEG_DTYPE)
print("\tsaving to file...")
if settings.options.D3:
trueinnii = nib.Nifti1Image(x_train[VALIDATION_SLICES, :, :, :], None)
truesegnii = nib.Nifti1Image(y_train[VALIDATION_SLICES, :, :, :], None)
truelivernii = nib.Nifti1Image(
y_train_liver[VALIDATION_SLICES, :, :, :], None)
truetumornii = nib.Nifti1Image(
y_train_tumor[VALIDATION_SLICES, :, :, :], None)
else:
trueinnii = nib.Nifti1Image(x_train[VALIDATION_SLICES, :, :], None)
truesegnii = nib.Nifti1Image(y_train[VALIDATION_SLICES, :, :], None)
truelivernii = nib.Nifti1Image(y_train_liver[VALIDATION_SLICES, :, :],
None)
truetumornii = nib.Nifti1Image(y_train_tumor[VALIDATION_SLICES, :, :],
None)
predsegnii = nib.Nifti1Image(y_pred_seg, None)
predfloatnii = nib.Nifti1Image(y_pred_float, None)
trueinnii.to_filename(logfileoutputdir + '/nii/trueimg.nii.gz')
truesegnii.to_filename(logfileoutputdir + '/nii/truseg.nii.gz')
truelivernii.to_filename(logfileoutputdir + '/nii/trueliver.nii.gz')
truetumornii.to_filename(logfileoutputdir + '/nii/truetumor.nii.gz')
predsegnii.to_filename(logfileoutputdir + '/nii/predtumorseg.nii.gz')
predfloatnii.to_filename(logfileoutputdir + '/nii/predtumorfloat.nii.gz')
print("\done saving.")
return modelloc
def TrainModel(idfold=0):
from setupmodel import GetSetupKfolds, GetCallbacks, GetOptimizer, GetLoss
from buildmodel import get_unet
###
### set up output, logging, and callbacks
###
kfolds = settings.options.kfolds
logfileoutputdir = '%s/%03d/%03d' % (settings.options.outdir, kfolds,
idfold)
os.system('mkdir -p ' + logfileoutputdir)
os.system('mkdir -p ' + logfileoutputdir + '/nii')
os.system('mkdir -p ' + logfileoutputdir + '/liver')
print("Output to\t", logfileoutputdir)
###
### load data
###
print('loading memory map db for large dataset')
numpydatabase = np.load(settings._globalnpfile)
(train_index, test_index,
valid_index) = GetSetupKfolds(settings.options.dbfile, kfolds, idfold)
print('copy data subsets into memory...')
axialbounds = numpydatabase['axialliverbounds']
dataidarray = numpydatabase['dataid']
dbtrainindex = np.isin(dataidarray, train_index)
dbtestindex = np.isin(dataidarray, test_index)
dbvalidindex = np.isin(dataidarray, valid_index)
subsetidx_train = np.all(np.vstack((axialbounds, dbtrainindex)), axis=0)
subsetidx_test = np.all(np.vstack((axialbounds, dbtestindex)), axis=0)
subsetidx_valid = np.all(np.vstack((axialbounds, dbvalidindex)), axis=0)
if np.sum(subsetidx_train) + np.sum(subsetidx_test) + np.sum(
subsetidx_valid) != min(np.sum(axialbounds), np.sum(dbtrainindex)):
raise ("data error: slice numbers dont match")
print('copy memory map from disk to RAM...')
trainingsubset = numpydatabase[subsetidx_train]
validsubset = numpydatabase[subsetidx_valid]
testsubset = numpydatabase[subsetidx_test]
# trimg = trainingsubset['imagedata']
# trseg = trainingsubset['truthdata']
# vaimg = validsubset['imagedata']
# vaseg = validsubset['truthdata']
# teimg = testsubset['imagedata']
# teseg = testsubset['truthdata']
# trimg_img = nib.Nifti1Image(trimg, None)
# trimg_img.to_filename( logfileoutputdir+'/nii/train-img.nii.gz')
# vaimg_img = nib.Nifti1Image(vaimg, None)
# vaimg_img.to_filename( logfileoutputdir+'/nii/valid-img.nii.gz')
# teimg_img = nib.Nifti1Image(teimg, None)
# teimg_img.to_filename( logfileoutputdir+'/nii/test-img.nii.gz')
#
# trseg_img = nib.Nifti1Image(trseg, None)
# trseg_img.to_filename( logfileoutputdir+'/nii/train-seg.nii.gz')
# vaseg_img = nib.Nifti1Image(vaseg, None)
# vaseg_img.to_filename( logfileoutputdir+'/nii/valid-seg.nii.gz')
# teseg_img = nib.Nifti1Image(teseg, None)
# teseg_img.to_filename( logfileoutputdir+'/nii/test-seg.nii.gz')
np.random.seed(seed=0)
np.random.shuffle(trainingsubset)
ntrainslices = len(trainingsubset)
nvalidslices = len(validsubset)
x_train = trainingsubset['imagedata']
y_train = trainingsubset['truthdata']
x_valid = validsubset['imagedata']
y_valid = validsubset['truthdata']
print("\nkfolds : ", kfolds)
print("idfold : ", idfold)
print("slices training : ", ntrainslices)
print("slices validation : ", nvalidslices)
###
### data preprocessing : applying liver mask
###
y_train_typed = y_train.astype(settings.SEG_DTYPE)
y_train_liver = preprocess.livermask(y_train_typed)
x_train_typed = x_train
x_train_typed = preprocess.window(x_train_typed, settings.options.hu_lb,
settings.options.hu_ub)
x_train_typed = preprocess.rescale(x_train_typed, settings.options.hu_lb,
settings.options.hu_ub)
y_valid_typed = y_valid.astype(settings.SEG_DTYPE)
y_valid_liver = preprocess.livermask(y_valid_typed)
x_valid_typed = x_valid
x_valid_typed = preprocess.window(x_valid_typed, settings.options.hu_lb,
settings.options.hu_ub)
x_valid_typed = preprocess.rescale(x_valid_typed, settings.options.hu_lb,
settings.options.hu_ub)
###
### create and run model
###
opt = GetOptimizer()
callbacks, modelloc = GetCallbacks(logfileoutputdir, "liver")
lss, met = GetLoss()
model = get_unet()
model.compile(loss=lss, metrics=met, optimizer=opt)
print("\n\n\tlivermask training...\tModel parameters: {0:,}".format(
model.count_params()))
if settings.options.augment:
train_datagen = ImageDataGenerator(
brightness_range=[0.9, 1.1],
preprocessing_function=preprocess.post_augment,
)
train_maskgen = ImageDataGenerator()
else:
train_datagen = ImageDataGenerator()
train_maskgen = ImageDataGenerator()
sd = 2 # arbitrary but fixed seed for ImageDataGenerators()
dataflow = train_datagen.flow(x_train_typed[..., np.newaxis],
batch_size=settings.options.trainingbatch,
seed=sd,
shuffle=True)
maskflow = train_maskgen.flow(y_train_liver[..., np.newaxis],
batch_size=settings.options.trainingbatch,
seed=sd,
shuffle=True)
train_generator = zip(dataflow, maskflow)
# train_generator = train_datagen.flow(x_train_typed[...,np.newaxis],
# y=y_train_liver[...,np.newaxis],
# batch_size=settings.options.trainingbatch,
# seed=sd,
# shuffle=True)
valid_datagen = ImageDataGenerator()
valid_maskgen = ImageDataGenerator()
validdataflow = valid_datagen.flow(
x_valid_typed[..., np.newaxis],
batch_size=settings.options.validationbatch,
seed=sd,
shuffle=True)
validmaskflow = valid_maskgen.flow(
y_valid_liver[..., np.newaxis],
batch_size=settings.options.validationbatch,
seed=sd,
shuffle=True)
valid_generator = zip(validdataflow, validmaskflow)
###
### visualize augmentation
###
#
# import matplotlib
# matplotlib.use('TkAgg')
# from matplotlib import pyplot as plt
# for i in range(8):
# plt.subplot(4,4,2*i + 1)
# imbatch = dataflow.next()
# sgbatch = maskflow.next()
# imaug = imbatch[0][:,:,0]
# sgaug = sgbatch[0][:,:,0]
# plt.imshow(imaug)
# plt.subplot(4,4,2*i + 2)
# plt.imshow(sgaug)
# plt.show()
# return
#
history_liver = model.fit_generator(
train_generator,
steps_per_epoch=ntrainslices / settings.options.trainingbatch,
epochs=settings.options.numepochs,
validation_data=valid_generator,
callbacks=callbacks,
shuffle=True,
validation_steps=nvalidslices / settings.options.validationbatch,
)
###
### make predicions on validation set
###
print("\n\n\tapplying models...")
y_pred_float = model.predict(x_valid_typed[..., np.newaxis])
y_pred_seg = (y_pred_float[..., 0] >=
settings.options.segthreshold).astype(settings.SEG_DTYPE)
print("\tsaving to file...")
trueinnii = nib.Nifti1Image(x_valid, None)
truesegnii = nib.Nifti1Image(y_valid, None)
# windownii = nib.Nifti1Image(x_valid_typed, None)
truelivernii = nib.Nifti1Image(y_valid_liver, None)
predsegnii = nib.Nifti1Image(y_pred_seg, None)
predfloatnii = nib.Nifti1Image(y_pred_float, None)
trueinnii.to_filename(logfileoutputdir + '/nii/trueimg.nii.gz')
truesegnii.to_filename(logfileoutputdir + '/nii/trueseg.nii.gz')
# windownii.to_filename( logfileoutputdir+'/nii/windowedimg.nii.gz')
truelivernii.to_filename(logfileoutputdir + '/nii/trueliver.nii.gz')
predsegnii.to_filename(logfileoutputdir + '/nii/predtumorseg.nii.gz')
predfloatnii.to_filename(logfileoutputdir + '/nii/predtumorfloat.nii.gz')
print("\done saving.")
return modelloc
def TrainModel(idfold=0, saveloclist=None):
from setupmodel import GetSetupKfolds, GetCallbacks, GetOptimizer, GetLoss
from buildmodel import get_unet
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'
###
### set up output, logging, and callbacks
###
kfolds = settings.options.kfolds
logfileoutputdir = '%s/%03d/%03d' % (settings.options.outdir, kfolds,
idfold)
os.system('mkdir -p ' + logfileoutputdir)
os.system('mkdir -p ' + logfileoutputdir + '/nii')
if settings.options.liver:
os.system('mkdir -p ' + logfileoutputdir + '/liver')
elif settings.options.tumor:
os.system('mkdir -p ' + logfileoutputdir + '/tumor')
else:
print('need to choose one of {liver,tumor}')
raise ValueError(
'need to choose to perform liver or tumor segmentation')
print("Output to\t", logfileoutputdir)
###
### load data
###
(train_index, test_index,
valid_index) = GetSetupKfolds(settings.options.dbfile, kfolds, idfold)
# if not saveloclist:
# saveloclist = setup_training_from_file()
if settings.options.liver:
saveloclist = settings.options.datafiles_liver
elif settings.options.tumor:
saveloclist = settings.options.datafiles_tumor
else:
saveloclist = settings.options.datafiles_liver
loclist = np.genfromtxt(saveloclist, delimiter=',', dtype='str')[1:]
trainingsubset = [row for row in loclist if int(row[0]) in train_index]
testingsubset = [row for row in loclist if int(row[0]) in test_index]
validsubset = [row for row in loclist if int(row[0]) in valid_index]
###
### create and run model
###
opt = GetOptimizer()
callbacks, modelloc = GetCallbacks(logfileoutputdir, "liver")
lss, met = GetLoss()
model = get_unet()
model.summary()
model.compile(loss=lss, metrics=met, optimizer=opt)
print("\n\n\tlivermask training...\tModel parameters: {0:,}".format(
model.count_params()))
train_xlist = [row[1] for row in trainingsubset]
train_ylist = [row[2] for row in trainingsubset]
valid_xlist = [row[1] for row in validsubset]
valid_ylist = [row[2] for row in validsubset]
training_generator = NpyDataGenerator(train_xlist, train_ylist)
validation_generator = NpyDataGenerator(valid_xlist, valid_ylist)
history_liver = model.fit_generator( \
verbose=2-int(settings.options.verbose),
generator=training_generator,
validation_data=validation_generator,
use_multiprocessing=True,
workers=8,
# steps_per_epoch = ntrainslices / settings.options.trainingbatch,
epochs = settings.options.numepochs,
callbacks = callbacks,
shuffle = True,
# validation_steps = nvalidslices / settings.options.validationbatch,
)
return modelloc
def TrainModel(idfold=0):
from setupmodel import GetSetupKfolds, GetCallbacks, GetOptimizer, GetLoss
from buildmodel import get_unet
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
###
### load data
###
kfolds = settings.options.kfolds
logfileoutputdir = '%s/%03d/%03d' % (settings.options.outdir, kfolds,
idfold)
os.system('mkdir -p ' + logfileoutputdir)
os.system('mkdir -p ' + logfileoutputdir + '/nii')
os.system('mkdir -p ' + logfileoutputdir + '/tumor')
print("Output to\t", logfileoutputdir)
print('loading memory map db for large dataset')
numpydatabase = np.load(settings._globalnpfile)
(train_index, test_index,
valid_index) = GetSetupKfolds(settings.options.dbfile, kfolds, idfold)
print('copy data subsets into memory...')
axialbounds = numpydatabase['axialtumorbounds']
dataidarray = numpydatabase['dataid']
dbtrainindex = np.isin(dataidarray, train_index)
dbtestindex = np.isin(dataidarray, test_index)
dbvalidindex = np.isin(dataidarray, valid_index)
subsetidx_train = np.all(np.vstack((axialbounds, dbtrainindex)), axis=0)
subsetidx_test = np.all(np.vstack((axialbounds, dbtestindex)), axis=0)
subsetidx_valid = np.all(np.vstack((axialbounds, dbvalidindex)), axis=0)
if np.sum(subsetidx_train) + np.sum(subsetidx_test) + np.sum(
subsetidx_valid) != min(np.sum(axialbounds), np.sum(dbtrainindex)):
raise ("data error: slice numbers dont match")
print('copy memory map from disk to RAM...')
trainingsubset = numpydatabase[subsetidx_train]
validsubset = numpydatabase[subsetidx_valid]
testsubset = numpydatabase[subsetidx_test]
del numpydatabase
del axialbounds
del dataidarray
del dbtrainindex
del dbtestindex
del dbvalidindex
del subsetidx_train
del subsetidx_test
del subsetidx_valid
np.random.seed(seed=0)
np.random.shuffle(trainingsubset)
ntrainslices = len(trainingsubset)
nvalidslices = len(validsubset)
x_train = trainingsubset['imagedata']
y_train = trainingsubset['truthdata']
x_valid = validsubset['imagedata']
y_valid = validsubset['truthdata']
print('\nkfolds : ', kfolds)
print("idfold : ", idfold)
print("slices training : ", ntrainslices)
print("slices validation : ", nvalidslices)
###
### data preprocessing : applying liver mask
###
y_train_typed = y_train.astype(settings.SEG_DTYPE)
y_train_liver = preprocess.livermask(y_train_typed)
y_train_tumor = preprocess.tumormask(y_train_typed)
x_train_typed = x_train
x_train_typed = preprocess.window(x_train_typed, settings.options.hu_lb,
settings.options.hu_ub)
x_train_typed = preprocess.rescale(x_train_typed, settings.options.hu_lb,
settings.options.hu_ub)
x_train_masked = x_train_typed * y_train_liver.astype(
settings.IMG_DTYPE) - (1.0 - y_train_liver.astype(settings.IMG_DTYPE))
y_valid_typed = y_valid.astype(settings.SEG_DTYPE)
y_valid_liver = preprocess.livermask(y_valid_typed)
y_valid_tumor = preprocess.tumormask(y_valid_typed)
x_valid_typed = x_valid
x_valid_typed = preprocess.window(x_valid_typed, settings.options.hu_lb,
settings.options.hu_ub)
x_valid_typed = preprocess.rescale(x_valid_typed, settings.options.hu_lb,
settings.options.hu_ub)
x_valid_masked = x_valid_typed * y_valid_liver.astype(
settings.IMG_DTYPE) - (1.0 - y_valid_liver.astype(settings.IMG_DTYPE))
###
### create and run model
###
opt = GetOptimizer()
callbacks, modelloc = GetCallbacks(logfileoutputdir, "tumor")
lss, met = GetLoss()
model = get_unet()
model.compile(loss=lss, metrics=met, optimizer=opt)
print("\n\n\tlivermask training...\tModel parameters: {0:,}".format(
model.count_params()))
if settings.options.augment:
train_datagen = ImageDataGenerator(
brightness_range=[0.9, 1.1],
fill_mode='nearest',
preprocessing_function=preprocess.post_augment)
train_maskgen = ImageDataGenerator()
else:
train_datagen = ImageDataGenerator()
train_maskgen = ImageDataGenerator()
test_datagen = ImageDataGenerator()
sd = 2
# dataflow = train_datagen.flow(x_train_typed[...,np.newaxis],
dataflow = train_datagen.flow(x_train_masked[..., np.newaxis],
batch_size=settings.options.trainingbatch,
seed=sd,
shuffle=True)
maskflow = train_maskgen.flow(y_train_tumor[..., np.newaxis],
batch_size=settings.options.trainingbatch,
seed=sd,
shuffle=True)
train_generator = zip(dataflow, maskflow)
valid_datagen = ImageDataGenerator()
valid_maskgen = ImageDataGenerator()
valid_dataflow = valid_datagen.flow(
x_valid_masked[..., np.newaxis],
batch_size=settings.options.validationbatch,
seed=sd,
shuffle=True)
valid_maskflow = valid_maskgen.flow(
y_valid_tumor[..., np.newaxis],
batch_size=settings.options.validationbatch,
seed=sd,
shuffle=True)
valid_generator = zip(valid_dataflow, valid_maskflow)
history_tumor = model.fit_generator(
train_generator,
steps_per_epoch=ntrainslices / settings.options.trainingbatch,
epochs=settings.options.numepochs,
validation_data=valid_generator,
callbacks=callbacks,
shuffle=True,
validation_steps=nvalidslices / settings.options.validationbatch,
)
del x_train
del y_train
del x_train_typed
del y_train_typed
del y_train_liver
del y_train_tumor
del x_train_masked
###
### make predicions on validation set
###
print("\n\n\tapplying models...")
y_pred_float = model.predict(x_valid_masked[..., np.newaxis])
y_pred_seg = (y_pred_float[..., 0] >=
settings.options.segthreshold).astype(settings.SEG_DTYPE)
print("\tsaving to file...")
trueinnii = nib.Nifti1Image(x_valid, None)
truesegnii = nib.Nifti1Image(y_valid, None)
truelivernii = nib.Nifti1Image(y_valid_liver, None)
truetumornii = nib.Nifti1Image(y_valid_tumor, None)
windownii = nib.Nifti1Image(x_valid_typed, None)
maskednii = nib.Nifti1Image(x_valid_masked, None)
predsegnii = nib.Nifti1Image(y_pred_seg, None)
predfloatnii = nib.Nifti1Image(y_pred_float, None)
trueinnii.to_filename(logfileoutputdir + '/nii/trueimg.nii.gz')
truesegnii.to_filename(logfileoutputdir + '/nii/trueseg.nii.gz')
truetumornii.to_filename(logfileoutputdir + '/nii/truetumor.nii.gz')
truelivernii.to_filename(logfileoutputdir + '/nii/trueliver.nii.gz')
windownii.to_filename(logfileoutputdir + '/nii/windowedimg.nii.gz')
maskednii.to_filename(logfileoutputdir + '/nii/masked.nii.gz')
predsegnii.to_filename(logfileoutputdir + '/nii/predtumorseg.nii.gz')
predfloatnii.to_filename(logfileoutputdir + '/nii/predtumorfloat.nii.gz')
del x_valid
del y_valid
del x_valid_typed
del y_valid_typed
del y_valid_liver
del y_valid_tumor
del x_valid_masked
print("\done saving.")
return modelloc
def TrainModel(idfold=0):
from setupmodel import GetSetupKfolds, GetCallbacks, GetOptimizer, GetLoss
from buildmodel import get_unet, thick_slices, unthick_slices, unthick
###
### set up output, logging and callbacks
###
kfolds = settings.options.kfolds
logfileoutputdir= '%s/%03d/%03d' % (settings.options.outdir, kfolds, idfold)
os.system ('mkdir -p ' + logfileoutputdir)
os.system ('mkdir -p ' + logfileoutputdir + '/nii')
os.system ('mkdir -p ' + logfileoutputdir + '/liver')
print("Output to\t", logfileoutputdir)
###
### load data
###
print('loading memory map db for large dataset')
numpydatabase = np.load(settings._globalnpfile)
(train_index,test_index,valid_index) = GetSetupKfolds(settings.options.dbfile, kfolds, idfold)
print('copy data subsets into memory...')
axialbounds = numpydatabase['axialliverbounds']
dataidarray = numpydatabase['dataid']
dbtrainindex = np.isin(dataidarray, train_index )
dbtestindex = np.isin(dataidarray, test_index )
dbvalidindex = np.isin(dataidarray, valid_index )
subsetidx_train = np.all( np.vstack((axialbounds , dbtrainindex)) , axis=0 )
subsetidx_test = np.all( np.vstack((axialbounds , dbtestindex )) , axis=0 )
subsetidx_valid = np.all( np.vstack((axialbounds , dbvalidindex)) , axis=0 )
print(np.sum(subsetidx_train) + np.sum(subsetidx_test) + np.sum(subsetidx_valid))
print(min(np.sum(axialbounds ),np.sum(dbtrainindex )))
if np.sum(subsetidx_train) + np.sum(subsetidx_test) + np.sum(subsetidx_valid) != min(np.sum(axialbounds ),np.sum(dbtrainindex )) :
raise("data error: slice numbers dont match")
print('copy memory map from disk to RAM...')
trainingsubset = numpydatabase[subsetidx_train]
validsubset = numpydatabase[subsetidx_valid]
testsubset = numpydatabase[subsetidx_test]
# np.random.seed(seed=0)
# np.random.shuffle(trainingsubset)
ntrainslices = len(trainingsubset)
nvalidslices = len(validsubset)
if settings.options.D3:
x_data = trainingsubset['imagedata']
y_data = trainingsubset['truthdata']
x_valid = validsubset['imagedata']
y_valid = validsubset['truthdata']
x_train = thick_slices(x_data, settings.options.thickness, trainingsubset['dataid'], train_index)
y_train = thick_slices(y_data, settings.options.thickness, trainingsubset['dataid'], train_index)
x_valid = thick_slices(x_valid, settings.options.thickness, validsubset['dataid'], valid_index)
y_valid = thick_slices(y_valid, settings.options.thickness, validsubset['dataid'], valid_index)
np.random.seed(seed=0)
train_shuffle = np.random.permutation(x_train.shape[0])
valid_shuffle = np.random.permutation(x_valid.shape[0])
x_train = x_train[train_shuffle,...]
y_train = y_train[train_shuffle,...]
x_valid = x_valid[valid_shuffle,...]
y_valid = y_valid[valid_shuffle,...]
elif settings.options.D25:
x_data = trainingsubset['imagedata']
y_data = trainingsubset['truthdata']
x_valid = validsubset['imagedata']
y_valid = validsubset['truthdata']
x_train = thick_slices(x_data, settings.options.thickness, trainingsubset['dataid'], train_index)
x_valid = thick_slices(x_valid, settings.options.thickness, validsubset['dataid'], valid_index)
y_train = thick_slices(y_data, 1, trainingsubset['dataid'], train_index)
y_valid = thick_slices(y_valid, 1, validsubset['dataid'], valid_index)
np.random.seed(seed=0)
train_shuffle = np.random.permutation(x_train.shape[0])
valid_shuffle = np.random.permutation(x_valid.shape[0])
x_train = x_train[train_shuffle,...]
y_train = y_train[train_shuffle,...]
x_valid = x_valid[valid_shuffle,...]
y_valid = y_valid[valid_shuffle,...]
else:
np.random.seed(seed=0)
np.random.shuffle(trainingsubset)
x_train=trainingsubset['imagedata']
y_train=trainingsubset['truthdata']
x_valid=validsubset['imagedata']
y_valid=validsubset['truthdata']
# slicesplit = int(0.9 * totnslice)
# TRAINING_SLICES = slice( 0, slicesplit)
# VALIDATION_SLICES = slice(slicesplit, totnslice )
print("\nkfolds : ", kfolds)
print("idfold : ", idfold)
print("slices training : ", ntrainslices)
print("slices validation : ", nvalidslices)
try:
print("slices testing : ", len(testsubset))
except:
print("slices testing : 0")
###
### data preprocessing : applying liver mask
###
y_train_typed = y_train.astype(settings.SEG_DTYPE)
y_train_liver = preprocess.livermask(y_train_typed)
x_train_typed = x_train
x_train_typed = preprocess.window(x_train_typed, settings.options.hu_lb, settings.options.hu_ub)
x_train_typed = preprocess.rescale(x_train_typed, settings.options.hu_lb, settings.options.hu_ub)
y_valid_typed = y_valid.astype(settings.SEG_DTYPE)
y_valid_liver = preprocess.livermask(y_valid_typed)
x_valid_typed = x_valid
x_valid_typed = preprocess.window(x_valid_typed, settings.options.hu_lb, settings.options.hu_ub)
x_valid_typed = preprocess.rescale(x_valid_typed, settings.options.hu_lb, settings.options.hu_ub)
# liver_idx = y_train_typed > 0
# y_train_liver = np.zeros_like(y_train_typed)
# y_train_liver[liver_idx] = 1
#
# tumor_idx = y_train_typed > 1
# y_train_tumor = np.zeros_like(y_train_typed)
# y_train_tumor[tumor_idx] = 1
#
# x_masked = x_train * y_train_liver - 100.0*(1.0 - y_train_liver)
# x_masked = x_masked.astype(settings.IMG_DTYPE)
###
### create and run model tf.keras.losses.mean_squared_error,
###
opt = GetOptimizer()
callbacks, modelloc = GetCallbacks(logfileoutputdir, "liver")
lss, met = GetLoss()
model = get_unet()
model.compile(loss = lss,
metrics = met,
optimizer = opt)
print("\n\n\tlivermask training...\tModel parameters: {0:,}".format(model.count_params()))
if settings.options.D3:
if settings.options.augment:
train_datagen = ImageDataGenerator3D(
brightness_range=[0.9,1.1],
width_shift_range=[-0.1,0.1],
height_shift_range=[-0.1,0.1],
horizontal_flip=True,
vertical_flip=True,
zoom_range=0.1,
fill_mode='nearest',
preprocessing_function=preprocess.post_augment
)
train_maskgen = ImageDataGenerator3D()
else:
train_datagen = ImageDataGenerator3D()
train_maskgen = ImageDataGenerator3D()
valid_datagen = ImageDataGenerator3D()
valid_maskgen = ImageDataGenerator3D()
else:
if settings.options.augment:
train_datagen = ImageDataGenerator2D(
brightness_range=[0.9,1.1],
width_shift_range=[-0.1,0.1],
height_shift_range=[-0.1,0.1],
horizontal_flip=True,
vertical_flip=True,
zoom_range=0.1,
fill_mode='nearest',
preprocessing_function=preprocess.post_augment
)
train_maskgen = ImageDataGenerator2D()
else:
train_datagen = ImageDataGenerator2D()
train_maskgen = ImageDataGenerator2D()
valid_datagen = ImageDataGenerator2D()
valid_maskgen = ImageDataGenerator2D()
sd = 2 # arbitrary but fixed seed for ImageDataGenerators()
if settings.options.D25:
dataflow = train_datagen.flow(x_train_typed,
batch_size=settings.options.trainingbatch,
seed=sd,
shuffle=True)
maskflow = train_maskgen.flow(y_train_liver,
batch_size=settings.options.trainingbatch,
seed=sd,
shuffle=True)
validdataflow = valid_datagen.flow(x_valid_typed,
batch_size=settings.options.validationbatch,
seed=sd,
shuffle=True)
validmaskflow = valid_maskgen.flow(y_valid_liver,
batch_size=settings.options.validationbatch,
seed=sd,
shuffle=True)
else:
dataflow = train_datagen.flow(x_train_typed[...,np.newaxis],
batch_size=settings.options.trainingbatch,
seed=sd,
shuffle=True)
maskflow = train_maskgen.flow(y_train_liver[...,np.newaxis],
batch_size=settings.options.trainingbatch,
seed=sd,
shuffle=True)
validdataflow = valid_datagen.flow(x_valid_typed[...,np.newaxis],
batch_size=settings.options.validationbatch,
seed=sd,
shuffle=True)
validmaskflow = valid_maskgen.flow(y_valid_liver[...,np.newaxis],
batch_size=settings.options.validationbatch,
seed=sd,
shuffle=True)
train_generator = zip(dataflow, maskflow)
valid_generator = zip(validdataflow, validmaskflow)
history_liver = model.fit_generator(
train_generator,
steps_per_epoch= ntrainslices // settings.options.trainingbatch,
validation_steps = nvalidslices // settings.options.validationbatch,
epochs=settings.options.numepochs,
validation_data=valid_generator,
callbacks=callbacks,
shuffle=True)
###
### make predicions on validation set
###
print("\n\n\tapplying models...")
if settings.options.D25:
y_pred_float = model.predict( x_valid_typed )[...,0] #[...,settings.options.thickness] )
else:
y_pred_float = model.predict( x_valid_typed[...,np.newaxis] )[...,0]
y_pred_seg = (y_pred_float >= settings.options.segthreshold).astype(settings.SEG_DTYPE)
if settings.options.D3:
x_valid = unthick(x_valid, settings.options.thickness, validsubset['dataid'], valid_index)
y_valid = unthick(y_valid, settings.options.thickness, validsubset['dataid'], valid_index)
y_valid_liver = unthick(y_valid_liver, settings.options.thickness, validsubset['dataid'], valid_index)
y_pred_float = unthick(y_pred_float, settings.options.thickness, validsubset['dataid'], valid_index)
y_pred_seg = unthick(y_pred_seg, settings.options.thickness, validsubset['dataid'], valid_index)
print("\tsaving to file...")
trueinnii = nib.Nifti1Image(x_valid, None)
truesegnii = nib.Nifti1Image(y_valid, None)
# windownii = nib.Nifti1Image(x_valid_typed, None)
truelivernii = nib.Nifti1Image(y_valid_liver, None)
predsegnii = nib.Nifti1Image(y_pred_seg, None )
predfloatnii = nib.Nifti1Image(y_pred_float, None)
trueinnii.to_filename( logfileoutputdir+'/nii/trueimg.nii.gz')
truesegnii.to_filename( logfileoutputdir+'/nii/truseg.nii.gz')
# windownii.to_filename( logfileoutputdir+'/nii/windowedimg.nii.gz')
truelivernii.to_filename( logfileoutputdir+'/nii/trueliver.nii.gz')
predsegnii.to_filename( logfileoutputdir+'/nii/predtumorseg.nii.gz')
predfloatnii.to_filename( logfileoutputdir+'/nii/predtumorfloat.nii.gz')
print("t\done saving.")
return modelloc
