registration_orchestrator#

class MiniBrain[source]#

Bases: TypedDict

A downscaled brain for quick visualization It includes the downscaled image and the scaling factors

array: ndarray#

scaling: tuple[float, float, float]#

class RegistrationProcessor(sample_manager: SampleManager, cfg_coordinator: ConfigCoordinator)[source]#

Bases: PipelineOrchestrator

This class is used to manage the registration process Perform image registration operations. Manage atlas setup and transformations. Handle registration configurations.

add_pipeline()[source]#

align(*args, _force=False, **kwargs)#

align_channel(channel)[source]#

channel_was_registered(channel)[source]#

channels_to_register()[source]#

channels_to_resample()[source]#

clear_landmarks(channel=None)[source]#: Clear (remove) the landmarks files

create_atlas_asset(annotator, channel_spec)[source]#

get(asset_type, channel='current', asset_sub_type=None, **kwargs)[source]#

Get an asset, automatically resolving registration template variables for asset types that require parametrisation (e.g. registration (Elastix) assets, where moving/fixed channels are conditional).

Parameters:

asset_type (str) – The asset type name.
channel (str) – The channel name.
asset_sub_type (str or None) – Optional sub-type.
**kwargs – Forwarded to the parent get.

Returns

Asset: The resolved asset.

get_align_with(channel)[source]#

get_aligned_image(channel)[source]#

get_atlas_files()[source]#

get_elx_asset(asset_type, channel)[source]#

get_fixed_image(channel)[source]#

get_fixed_moving_channels(channel)[source]#

get_img_to_register(channel, other_channel)[source]#

get_moving_channel(channel: str) → str[source]#

Get the moving channel for a given channel

Warning

Contrary to get_fixed_moving_channels, this method does not check for the existence of the fixed channel. It simply returns the moving channel as specified in the config.

Parameters:: channel (str) – The channel to get the moving channel for

Returns

str: The moving channel

get_moving_image(channel)[source]#

get_registration_sequence_channels(first_channel, stop_channel='atlas')[source]#

parametrize_assets()[source]#

plot_atlas(channel)[source]#

plot_registration_results(channel, composite=False, parent=None)[source]#

project_mini_brain(channel)[source]#

Project the mini brain of the channel as a mask and a surface projection

Parameters:: channel (str) – The channel to project

Returns

np.ndarray, np.ndarray: The mask and the projection

registration_status()[source]#

resample_channel(channel, increment_main=False)[source]#

resample_for_registration(*args, _force=False, **kwargs)#

setup(sample_manager: SampleManager | None = None)[source]#

Attach a sample manager and mark this processor as ready.

Parameters:: sample_manager (SampleManager, optional) – If provided, replaces the currently stored sample manager. When None, the previously stored instance is reused.
Raises:: ValueError – If the config section identified by config_name is absent.

setup_atlases(event=None)[source]#

update_atlas_asset(channel, annotator=None)[source]#

annotators: Dict[str, Annotation]#

property channels#

config_name = 'registration'#

mini_brains: Dict[str, MiniBrain]#

property n_registration_steps#

progress_watcher: 'ProgressWatcher' | None#

property ref_channel_cfg#

property registration_params_files#

sample_manager: SampleManager#

setup_complete: bool#

property was_registered#

class RegistrationStatus(value)[source]#

Bases: Enum

MISSING_OUTPUTS = 1#

NOT_SELECTED = 0#

REGISTERED = 2#

setup_mini_brain(atlas_base_name, mini_brain_scaling=(5, 5, 5))[source]#

Create a downsampled version of the Allen Brain Atlas for the mini brain widget

Parameters:: mini_brain_scaling (tuple(int, int, int)) – The scaling factors for the mini brain. Default is (5, 5, 5)

Returns

tuple(scale, downsampled_array)