def cvpr2018_net(vol_size, enc_nf, dec_nf, full_size=True, indexing='ij'):
"""
From https://github.com/voxelmorph/voxelmorph.
unet architecture for voxelmorph models presented in the CVPR 2018 paper.
You may need to modify this code (e.g., number of layers) to suit your project needs.
:param vol_size: volume size. e.g. (256, 256, 256)
:param enc_nf: list of encoder filters. right now it needs to be 1x4.
e.g. [16,32,32,32]
:param dec_nf: list of decoder filters. right now it must be 1x6 (like voxelmorph-1) or 1x7 (voxelmorph-2)
:return: the keras model
"""
import keras.layers as KL
from keras.initializers import RandomNormal
ndims = len(vol_size)
assert ndims==3, "ndims should be 3. found: %d" % ndims
src = Input(vol_size + (1,), name='input_src')
tgt = Input(vol_size + (1,), name='input_tgt')
input_stack = Concatenate(name='concat_inputs')([src, tgt])
# get the core model
x = unet3D(input_stack, img_shape=vol_size, out_im_chans=ndims, nf_enc=enc_nf, nf_dec=dec_nf)
# transform the results into a flow field.
Conv = getattr(KL, 'Conv%dD' % ndims)
flow = Conv(ndims, kernel_size=3, padding='same', name='flow',
kernel_initializer=RandomNormal(mean=0.0, stddev=1e-5))(x)
# warp the source with the flow
y = SpatialTransformer(interp_method='linear', indexing=indexing)([src, flow])
# prepare model
model = Model(inputs=[src, tgt], outputs=[y, flow])
return model
def color_delta_unet_model(img_shape,
n_output_chans,
model_name='color_delta_unet',
enc_params=None,
include_aux_input=False,
aux_input_shape=None,
do_warp_to_target_space=False):
x_src = Input(img_shape, name='input_src')
x_tgt = Input(img_shape, name='input_tgt')
if aux_input_shape is None:
aux_input_shape = img_shape
x_seg = Input(aux_input_shape, name='input_src_aux')
inputs = [x_src, x_tgt, x_seg]
if do_warp_to_target_space: # warp transformed vol to target space in the end
n_dims = len(img_shape) - 1
flow_srctotgt = Input(img_shape[:-1] + (n_dims, ), name='input_flow')
inputs += [flow_srctotgt]
if include_aux_input:
unet_inputs = [x_src, x_tgt, x_seg]
unet_input_shape = img_shape[:-1] + (img_shape[-1] * 2 +
aux_input_shape[-1], )
else:
unet_inputs = [x_src, x_tgt]
unet_input_shape = img_shape[:-1] + (img_shape[-1] * 2, )
x_stacked = Concatenate(axis=-1)(unet_inputs)
n_dims = len(img_shape) - 1
if n_dims == 2:
color_delta = unet2D(
x_stacked,
unet_input_shape,
n_output_chans,
nf_enc=enc_params['nf_enc'],
nf_dec=enc_params['nf_dec'],
n_convs_per_stage=enc_params['n_convs_per_stage'],
)
conv_fn = Conv2D
else:
color_delta = unet3D(
x_stacked,
unet_input_shape,
n_output_chans,
nf_enc=enc_params['nf_enc'],
nf_dec=enc_params['nf_dec'],
n_convs_per_stage=enc_params['n_convs_per_stage'],
)
conv_fn = Conv3D
# last conv to get the output shape that we want
color_delta = conv_fn(n_output_chans,
kernel_size=3,
padding='same',
name='color_delta')(color_delta)
transformed_out = Add(name='add_color_delta')([x_src, color_delta])
if do_warp_to_target_space:
transformed_out = SpatialTransformer(indexing='xy')(
[transformed_out, flow_srctotgt])
# kind of silly, but do a reshape so keras doesnt complain about returning an input
x_seg = Reshape(aux_input_shape, name='aux')(x_seg)
return Model(inputs=inputs,
outputs=[color_delta, transformed_out, x_seg],
name=model_name)