niftynet.application.label_driven_registration module

A preliminary re-implementation of:
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
The original implementation and tutorial is available at:
https://github.com/YipengHu/label-reg
class RegApp(net_param, action_param, action)[source]

Bases: niftynet.application.base_application.BaseApplication

REQUIRED_CONFIG_SECTION = 'REGISTRATION'
initialise_dataset_loader(data_param=None, task_param=None, data_partitioner=None)[source]

this function initialise self.readers

Parameters:
  • data_param – input modality specifications
  • task_param – contains task keywords for grouping data_param
  • data_partitioner – specifies train/valid/infer splitting if needed
Returns:

initialise_sampler()[source]

Samplers take self.reader as input and generates sequences of ImageWindow that will be fed to the networks

This function sets self.sampler.

initialise_network()[source]

This function create an instance of network and sets self.net

Returns:None
connect_data_and_network(outputs_collector=None, gradients_collector=None)[source]

Adding sampler output tensor and network tensors to the graph.

Parameters:
  • outputs_collector
  • gradients_collector
Returns:

interpret_output(batch_output)[source]

Implement output interpretations, e.g., save to hard drive cache output windows.

Parameters:batch_output – outputs by running the tf graph
Returns:True indicates the driver should continue the loop False indicates the drive should stop