def golkov_multi(data, n_directions, random_seed=400):
max_path = (
"/v/raid1b/egibbons/MRIdata/DTI/noddi/max_values_%i_directions_1d.h5" %
n_directions)
maxs = readhdf5.read_hdf5(max_path, "max_values")[None, None, None, :]
max_y_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/max_y_1d.h5"
max_y = readhdf5.read_hdf5(max_y_path, "max_y")
subsampling_pattern = subsampling.gensamples(n_directions,
random_seed=random_seed)
image_size = (n_directions, )
model = model1d_multi.fc_1d(image_size)
model.compile(optimizer=Adam(lr=1e-3,
beta_1=0.99,
beta_2=0.995,
epsilon=1e-08,
decay=0.85),
loss="mean_absolute_error",
metrics=["accuracy"])
model.load_weights("/v/raid1b/egibbons/models/noddi-%i_golkov_multi.h5" %
n_directions)
print("golkov_multi model loaded")
data_subsampled = data[:, :, :, subsampling_pattern]
data_subsampled /= maxs
dim0, dim1, n_slices, n_directions = data_subsampled.shape
print("Predicting...")
start = time.time()
# data_subsampled_temp = data_subsampled.reshape(0,1)
x = data_subsampled.reshape(n_slices * dim0 * dim1, -1)
recon = model.predict(x, batch_size=10000)
prediction = recon.reshape(dim0, dim1, n_slices, 4)
for ii in range(4):
prediction[:, :, :, ii] *= max_y[ii]
print("Predictions completed...took: %f" % (time.time() - start))
return prediction
def model_raw(data, n_directions, random_seed=400, loss_type="l1"):
image_size = (128, 128, n_directions)
model = simple2d.res2d(image_size)
model.compile(optimizer=Adam(lr=1e-3),
loss="mean_absolute_error",
metrics=["accuracy"])
model.load_weights("/v/raid1b/egibbons/models/noddi-%i_raw.h5" %
(n_directions))
print("2D dense model loaded for raw data. Using %s loss" % loss_type)
max_path = (
"/v/raid1b/egibbons/MRIdata/DTI/noddi/max_values_%i_directions_raw.h5"
% n_directions)
maxs = readhdf5.read_hdf5(max_path, "max_values")[None, None, None, :]
max_y_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/max_y_raw.h5"
max_y = readhdf5.read_hdf5(max_y_path, "max_y")
subsampling_pattern = subsampling.gensamples(n_directions,
random_seed=random_seed)
x = data[:, :, :, subsampling_pattern]
x = x.transpose(2, 0, 1, 3)
x /= maxs.squeeze()[None, None, None, :]
print("Predicting 2D...")
start = time.time()
prediction = model.predict(x, batch_size=10).transpose(1, 2, 0, 3)
print("Predictions completed...took: %f" % (time.time() - start))
### DISPLAY ###
diffusivity_scaling = 1
for ii in range(4):
prediction[:, :, :, ii] *= max_y[ii]
prediction[:, :, :, 3] /= diffusivity_scaling
return prediction
def train(n_directions):
loss_type = "l1"
print("running 2D network with %s loss and %i directions" %
(loss_type, n_directions))
n_gpu = 1
n_epochs = 100
batch_size = 10
learning_rate = 1e-3
image_size = (128, 128, n_directions)
model = simple2d.res2d(image_size)
optimizer = Adam(lr=learning_rate)
if loss_type == "l1":
model.compile(optimizer=optimizer, loss="mean_absolute_error")
else:
model.compile(optimizer=optimizer, loss=network_utils.perceptual_loss)
### DATA LOADING ###
x_path = ("/v/raid1b/egibbons/MRIdata/DTI/noddi/x_%i_directions_2d.h5" %
n_directions)
y_gfa_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/y_gfa_2d.h5"
print("Loading data...")
start = time.time()
y_gfa = readhdf5.read_hdf5(y_gfa_path, "y_gfa")
y = y_gfa
x = readhdf5.read_hdf5(x_path, "x_%i_directions" % n_directions)
print("Data is loaded...took: %f seconds" % (time.time() - start))
### MODEL FITTING ###
batch_size_multi_gpu = n_gpu * batch_size
tensorboard = TensorBoard(log_dir='./logs',
histogram_freq=1,
batch_size=batch_size_multi_gpu,
write_graph=True,
write_grads=True,
write_images=True,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None)
save_path = ("/v/raid1b/egibbons/models/noddi-%i_2d_gfa_no_scale.h5" %
(n_directions))
print("saving to: %s" % save_path)
checkpointer = ModelCheckpoint(filepath=save_path,
verbose=1,
save_best_only=True,
save_weights_only=True,
period=1)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
min_lr=1e-7)
lrate = LearningRateScheduler(network_utils.step_decay)
stopping = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=20,
verbose=0,
mode='auto')
model.fit(
x=x,
y=y,
batch_size=batch_size_multi_gpu,
epochs=n_epochs,
verbose=2,
callbacks=[checkpointer, lrate, stopping],
validation_split=0.2,
shuffle=True,
)
print("trained %i direction model" % n_directions)
sys.path.append("/home/mirl/egibbons/noddi")
from noddi_utils import metrics
from noddi_utils import noddistudy
from utils import display
from utils import readhdf5
test_cases = [
"P032315", "P061815", "P020916", "N011118A", "N011118B", "P072216",
"P082616"
]
directions = [128, 64, 32, 24, 16]
max_y_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/max_y_2d.h5"
max_y = readhdf5.read_hdf5(max_y_path, "max_y")
data = {}
models = ["2d", "separate_2d"]
data_types = ["odi", "fiso", "ficvf", "gfa"]
measurements = ["SSIM", "PSNR", "NRMSE"]
for model_type in models:
data[model_type] = {}
for data_type in data_types:
data[model_type][data_type] = {}
for measurement_type in measurements:
data[model_type][data_type][measurement_type] = ([], [])
from utils import readhdf5
n_directions = 32
x_path = ("/v/raid1b/egibbons/MRIdata/DTI/noddi/x_%i_directions_2d.h5" %
n_directions)
y_odi_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/y_odi_2d.h5"
y_fiso_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/y_fiso_2d.h5"
y_ficvf_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/y_ficvf_2d.h5"
y_gfa_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/y_gfa_2d.h5"
print("Loading data...")
start = time.time()
y_odi = readhdf5.read_hdf5(y_odi_path,"y_odi")
y_fiso = readhdf5.read_hdf5(y_fiso_path,"y_fiso")
y_ficvf = readhdf5.read_hdf5(y_ficvf_path,"y_ficvf")
y_gfa = readhdf5.read_hdf5(y_gfa_path,"y_gfa")
x = readhdf5.read_hdf5(x_path,"x_%i_directions" % n_directions).transpose(0,2,1,3)[:,::-1,::-1,:]
y = np.concatenate((y_odi, y_fiso, y_ficvf, y_gfa),
axis=3).transpose(0,2,1,3)[:,::-1,::-1,:]
for ii in range(x.shape[3]):
plt.figure()
plt.imshow(x[25,:,:,ii].squeeze(),cmap="gray")
plt.axis("off")
plt.axis("equal")
plt.savefig("../results/x_%i.pdf" % ii, bbox_inches="tight")
def train(n_directions):
print("running network with %i directions" % n_directions)
n_gpu = 1
n_epochs = 100
batch_size = 10000
learning_rate = 1e-3
image_size = (n_directions, )
model = model1d.fc_1d(image_size)
optimizer = Adam(lr=learning_rate)
model.compile(
optimizer=optimizer,
loss="mean_squared_error",
)
x_path = ("/v/raid1b/egibbons/MRIdata/DTI/noddi/x_%i_directions_1d.h5" %
n_directions)
y_odi_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/y_odi_1d.h5"
y_fiso_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/y_fiso_1d.h5"
y_ficvf_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/y_ficvf_1d.h5"
y_gfa_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/y_gfa_1d.h5"
print("Loading data...")
x = readhdf5.read_hdf5(x_path, "x_%i_directions" % n_directions)
y_odi = readhdf5.read_hdf5(y_odi_path, "y_odi")
y_fiso = readhdf5.read_hdf5(y_fiso_path, "y_fiso")
y_ficvf = readhdf5.read_hdf5(y_ficvf_path, "y_ficvf")
y_gfa = readhdf5.read_hdf5(y_gfa_path, "y_gfa")
print("Data is loaded...")
n_samples, _ = x.shape
print(y_odi.shape)
print(y_fiso.shape)
print(y_ficvf.shape)
print(y_gfa.shape)
y = np.concatenate((y_odi, y_fiso, y_ficvf, y_gfa), axis=1)
batch_size_multi_gpu = n_gpu * batch_size
tensorboard = TensorBoard(log_dir='./logs',
histogram_freq=1,
batch_size=batch_size_multi_gpu,
write_graph=True,
write_grads=True,
write_images=True,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None)
save_path = ("/v/raid1b/egibbons/models/noddi-%i_golkov_multi.h5" %
n_directions)
checkpointer = ModelCheckpoint(filepath=save_path,
verbose=1,
monitor="val_loss",
save_best_only=True,
save_weights_only=True,
period=25)
reduce_lr = ReduceLROnPlateau(monitor="val_loss",
factor=0.1,
patience=5,
min_lr=1e-7)
stopping = EarlyStopping(monitor="val_loss",
min_delta=0,
patience=30,
verbose=0,
mode='auto')
model.fit(
x=x,
y=y,
batch_size=batch_size_multi_gpu,
epochs=n_epochs,
verbose=2,
callbacks=[checkpointer, reduce_lr, stopping],
validation_split=0.2,
shuffle=True,
)
print("trained %i direction model" % n_directions)
def separate_2d(data,
n_directions,
random_seed=400,
loss_type="l1",
scaling=True):
# load the data
max_path = (
"/v/raid1b/egibbons/MRIdata/DTI/noddi/max_values_%i_directions_2d.h5" %
n_directions)
maxs = readhdf5.read_hdf5(max_path, "max_values")[None, None, None, :]
max_y_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/max_y_2d.h5"
max_y = readhdf5.read_hdf5(max_y_path, "max_y")
subsampling_pattern = subsampling.gensamples(n_directions,
random_seed=random_seed)
x = data[:, :, :, subsampling_pattern]
x = x.transpose(2, 0, 1, 3)
x /= maxs.squeeze()[None, None, None, :]
x_noddi = np.copy(x)
x_gfa = np.copy(x)
image_size = (128, 128, n_directions)
# noddi model
model_noddi = simple2d_noddi.res2d(image_size)
model_noddi.compile(optimizer=Adam(lr=1e-3),
loss="mean_absolute_error",
metrics=["accuracy"])
model_noddi.load_weights("/v/raid1b/egibbons/models/noddi-%i_2d_noddi.h5" %
(n_directions))
model_gfa = simple2d_gfa.res2d(image_size)
model_gfa.compile(optimizer=Adam(lr=1e-3),
loss="mean_absolute_error",
metrics=["accuracy"])
if scaling is True:
model_gfa.load_weights("/v/raid1b/egibbons/models/noddi-%i_2d_gfa.h5" %
(n_directions))
scaling_factor = 5
else:
print("no scaling")
model_gfa.load_weights(
"/v/raid1b/egibbons/models/noddi-%i_2d_gfa_no_scale.h5" %
(n_directions))
scaling_factor = 1
print("2D dense model loaded. Using %s loss" % loss_type)
print("Predicting 2D separate...")
start = time.time()
prediction_noddi = model_noddi.predict(x_noddi, batch_size=10).transpose(
1, 2, 0, 3)
prediction_gfa = model_gfa.predict(x_gfa,
batch_size=10).transpose(1, 2, 0, 3)
print("Predictions completed...took: %f" % (time.time() - start))
prediction = np.concatenate(
(prediction_noddi, prediction_gfa / scaling_factor), axis=3)
### DISPLAY ###
for ii in range(4):
prediction[:, :, :, ii] *= max_y[ii]
return prediction
from noddi_utils import noddistudy from noddi_utils import subsampling from noddi_utils import predict from utils import display from utils import readhdf5 loss_type = "l1" n_channels = 64 ### LOAD DATA ### patient_number = "P111816" noddi_data = noddistudy.NoddiData(patient_number) max_y_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/max_y_2d.h5" max_y = readhdf5.read_hdf5(max_y_path, "max_y") data_full = noddi_data.get_full() data_odi = noddi_data.get_odi() data_fiso = noddi_data.get_fiso() data_ficvf = noddi_data.get_ficvf() data_gfa = noddi_data.get_gfa() # data_md = noddi_data.get_md() # data_ad = noddi_data.get_ad() # data_fa = noddi_data.get_fa() data_odi[data_odi > max_y[0]] = max_y[0] data_fiso[data_fiso > max_y[1]] = max_y[1] data_ficvf[data_ficvf > max_y[2]] = max_y[2] data_gfa[data_gfa > max_y[3]] = max_y[3] # data_md[data_md>max_y[4]] = max_y[4]
import numpy as np
sys.path.append("/home/mirl/egibbons/noddi")
from noddi_utils import noddistudy
from recon import imtools
from utils import matutils
from utils import readhdf5
patient_number = "P080715"
slice_use = 20
directions = [128, 64, 32, 24, 16, 8]
max_y_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/max_y_2d.h5"
max_y = readhdf5.read_hdf5(max_y_path, "max_y")
data = {}
models = ["2d", "separate_no_scale_2d"]
noddi_data = noddistudy.NoddiData(patient_number)
data_gfa = noddi_data.get_gfa()[:, :, slice_use].transpose(1, 0)[::-1, ::-1]
data_gfa[data_gfa > max_y[3]] = max_y[3]
crop_factor = 100
roll_factor = -5
data_gfa = np.roll(data_gfa, roll_factor, axis=0)
data_gfa = imtools.Crop(data_gfa, crop_factor, crop_factor)
data_gfa /= 0.5
range_gfa = np.amax(data_gfa) - np.amin(data_gfa)
n_directions = 24
sampling_pattern = subsampling.gensamples(n_directions)
### LOAD DATA ###
patient_number = test_cases[0]
noddi_data = noddistudy.NoddiData(patient_number)
data_full = noddi_data.get_full()
data_odi = noddi_data.get_odi().transpose(1,0,2)[::-1,::-1]
data_fiso = noddi_data.get_fiso().transpose(1,0,2)[::-1,::-1]
data_ficvf = noddi_data.get_ficvf().transpose(1,0,2)[::-1,::-1]
data_gfa = noddi_data.get_gfa().transpose(1,0,2)[::-1,::-1]
max_y_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/max_y_2d.h5"
max_y = readhdf5.read_hdf5(max_y_path,"max_y")
### LOAD MODEL ###
image_size = (128,128,n_directions)
model = simple2d.res2d(image_size)
model.compile(optimizer=Adam(lr=1e-3),
loss="mean_absolute_error",
metrics=["accuracy"])
model.load_weights("noddi_test-%i_2d.h5" %
(n_directions))
print("2D dense model loaded. Using %s loss" % loss_type)
max_path = ("/v/raid1b/egibbons/MRIdata/DTI/noddi/"
