def __init__(self,
decay,
affine_w_initializer=None,
affine_b_initializer=None,
disp_w_initializer=None,
disp_b_initializer=None,
acti_func='relu',
interp='linear',
boundary='replicate',
name='inet-hybrid-pre-warp'):
"""
:param decay: float, regularisation decay
:param affine_w_initializer: weight initialisation for affine registration network
:param affine_b_initializer: bias initialisation for affine registration network
:param disp_w_initializer: weight initialisation for dense registration network
:param disp_b_initializer: bias initialisation for dense registration network
:param acti_func: activation function to use
:param interp: string, type of interpolation for the resampling [default:linear]
:param boundary: string, padding mode to deal with image boundary
:param name: layer name
"""
BaseNet.__init__(self, name=name)
self.global_net = INetAffine(decay=decay,
affine_w_initializer=affine_w_initializer,
affine_b_initializer=affine_b_initializer,
acti_func=acti_func,
name='inet-global')
self.local_net = INetDense(decay=decay,
disp_w_initializer=disp_w_initializer,
disp_b_initializer=disp_b_initializer,
acti_func=acti_func,
name='inet-local')
self.interp = interp
self.boundary = boundary
def test_3d_shape(self):
input_shape = (2, 32, 32, 32, 1)
x = tf.ones(input_shape)
densenet_instance = INetDense()
out = densenet_instance(x, x, is_training=True)
print(densenet_instance)
with self.cached_session() as sess:
sess.run(tf.global_variables_initializer())
out = sess.run(out)
self.assertAllClose((2, 32, 32, 32, 3), out.shape)
def __init__(self,
decay,
affine_w_initializer=None,
affine_b_initializer=None,
disp_w_initializer=None,
disp_b_initializer=None,
acti_func='relu',
interp='linear',
boundary='replicate',
name='inet-hybrid-pre-warp'):
"""
Re-implementation of the registration network proposed in:
Hu et al., Label-driven weakly-supervised learning for
multimodal deformable image registration, arXiv:1711.01666
https://arxiv.org/abs/1711.01666
Hu et al., Weakly-Supervised Convolutional Neural Networks for
Multimodal Image Registration, Medical Image Analysis (2018)
https://doi.org/10.1016/j.media.2018.07.002
see also:
https://github.com/YipengHu/label-reg
:param decay:
:param affine_w_initializer:
:param affine_b_initializer:
:param disp_w_initializer:
:param disp_b_initializer:
:param acti_func:
:param interp:
:param boundary:
:param name:
"""
BaseNet.__init__(self, name=name)
self.global_net = INetAffine(decay=decay,
affine_w_initializer=affine_w_initializer,
affine_b_initializer=affine_b_initializer,
acti_func=acti_func,
name='inet-global')
self.local_net = INetDense(decay=decay,
disp_w_initializer=disp_w_initializer,
disp_b_initializer=disp_b_initializer,
acti_func=acti_func,
name='inet-local')
self.interp = interp
self.boundary = boundary
def __init__(self,
decay,
affine_w_initializer=None,
affine_b_initializer=None,
disp_w_initializer=None,
disp_b_initializer=None,
acti_func='relu',
interp='linear',
boundary='replicate',
name='inet-hybrid-pre-warp'):
"""
Re-implementation of the registration network proposed in:
Hu et al., Label-driven weakly-supervised learning for
multimodal deformable image registration, arXiv:1711.01666
https://arxiv.org/abs/1711.01666
:param decay:
:param affine_w_initializer:
:param affine_b_initializer:
:param disp_w_initializer:
:param disp_b_initializer:
:param acti_func:
:param interp:
:param boundary:
:param name:
"""
BaseNet.__init__(self, name=name)
self.global_net = INetAffine(decay=decay,
affine_w_initializer=affine_w_initializer,
affine_b_initializer=affine_b_initializer,
acti_func=acti_func,
name='inet-global')
self.local_net = INetDense(decay=decay,
disp_w_initializer=disp_w_initializer,
disp_b_initializer=disp_b_initializer,
acti_func=acti_func,
name='inet-local')
self.interp = interp
self.boundary = boundary
def __init__(self,
decay,
affine_w_initializer=None,
affine_b_initializer=None,
disp_w_initializer=None,
disp_b_initializer=None,
acti_func='relu',
interp='linear',
boundary='replicate',
name='inet-hybrid-two-stream'):
BaseNet.__init__(self, name=name)
self.global_net = INetAffine(decay=decay,
affine_w_initializer=affine_w_initializer,
affine_b_initializer=affine_b_initializer,
acti_func=acti_func,
name='inet-global')
self.local_net = INetDense(decay=decay,
disp_w_initializer=disp_w_initializer,
disp_b_initializer=disp_b_initializer,
acti_func=acti_func,
name='inet-local')
self.interp = interp
self.boundary = boundary