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
from noddi_utils import noddistudy
from noddi_utils import subsampling
### SETUP ###
with open("noddi_metadata.json") as metadata:
patient_database = json.load(metadata)
print("We have %i cases" % len(patient_database))
directions = [128, 64, 32, 24, 16, 8]
### MAIN LOOP ###
for num_directions in directions:
print("Generating 1D data for %i directions" % num_directions)
subsampling_indices = subsampling.gensamples(num_directions)
num_cases = len(patient_database)
print("\n")
mm = 0
for patient_number in sorted(patient_database.keys()):
noddi_data = noddistudy.NoddiData(patient_number)
if (noddi_data.get_type() == "test"
or noddi_data.get_type() == "duplicate"):
continue
print("Currently reading: %s as %s data" %
(patient_number, noddi_data.get_type()))
return data_patches
### SETUP ###
with open("noddi_metadata.json") as metadata:
patient_database = json.load(metadata)
print("We have %i cases" % len(patient_database))
seeds = [100, 225, 300, 325, 400, 425, 500, 525, 600]
### MAIN LOOP ###
for random_seed in seeds:
num_directions = 24
print("Generating 2D data for %i directions" % num_directions)
subsampling_indices = subsampling.gensamples(num_directions,
random_seed=random_seed)
print("Processing %i directions" % len(subsampling_indices))
### DATA LOADING AND AUGMENTATION ###
print("\n")
ii = 0
for patient_number in sorted(patient_database.keys()):
noddi_data = noddistudy.NoddiData(patient_number)
if (noddi_data.get_type() == "test"
or noddi_data.get_type() == "duplicate"):
continue
print("Currently reading: %s as %s data" %
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 model_2d import dense2d from model_2d import simple2d from model_2d import unet2d from noddi_utils import network_utils from noddi_utils import noddistudy from noddi_utils import predict from noddi_utils import subsampling from recon import imtools from utils import readhdf5 from utils import display test_cases = ["P032315", "P080715", "P061114", "N011118A", "N011118B"] n_directions = 24 sampling_pattern = subsampling.gensamples(n_directions, random_seed=425) ### LOAD DATA ### patient_number = test_cases[0] 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_raw = noddi_data.get_raw() 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]
from matplotlib import pyplot as plt
sys.path.append("/home/mirl/egibbons/noddi")
from model_2d import simple2d
from noddi_utils import noddistudy
from noddi_utils import subsampling
from recon import imtools
from utils import readhdf5
test_cases = ["P032315","P080715","P061114",
"N011118A","N011118B"]
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 ###