# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function
from niftynet.layer.resampler import ResamplerLayer as resampler
from niftynet.network.base_net import BaseNet
from niftynet.network.interventional_affine_net import INetAffine
from niftynet.network.interventional_dense_net import INetDense
[docs]class INetHybridPreWarp(BaseNet):
[docs] 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
[docs] def layer_op(self,
fixed_image,
moving_image,
is_training=True,
**unused_kwargs):
affine_field = self.global_net(fixed_image, moving_image, is_training)
moving_image = resampler(
interpolation=self.interp,
boundary=self.boundary)(moving_image, affine_field)
dense_field = self.local_net(
fixed_image, moving_image, affine_field, is_training)
return dense_field, affine_field
[docs]class INetHybridTwoStream(BaseNet):
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
[docs] def layer_op(self,
fixed_image,
moving_image,
is_training=True,
**unused_kwargs):
affine_field = self.global_net(fixed_image, moving_image, is_training)
dense_field = self.local_net(fixed_image, moving_image, is_training)
return dense_field + affine_field, dense_field, affine_field
