niftynet.network.interventional_dense_net module

class INetDense(decay=0.0, smoothing=0, disp_w_initializer=None, disp_b_initializer=None, acti_func='relu', multi_scale_fusion=True, name='inet-dense')

Bases: niftynet.network.base_net.BaseNet

__init__(decay=0.0, smoothing=0, disp_w_initializer=None, disp_b_initializer=None, acti_func='relu', multi_scale_fusion=True, name='inet-dense')

The network estimates dense displacement fields from a pair of moving and fixed images:

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

Parameters:

• decay –
• smoothing –
• disp_w_initializer – initialisation of the displacement fields
• disp_b_initializer – initialisation of the dis
• acti_func –
• multi_scale_fusion – True/False indicating whether to use multiscale feature fusion.
• name –

layer_op(fixed_image, moving_image, base_grid=None, is_training=True, **unused_kwargs)

Parameters:

• fixed_image –
• moving_image –
• base_grid –
• is_training –

Returns:

estimated dense displacement fields