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Description:

itkdreg 0.0.1

itk-dreg
A framework for distributed, large-scale image registration.
Overview
The ITK Distributed Registration module (itk-dreg) provides a framework based on the
Insight Toolkit (ITK) and the dask.distributed library for the purpose of registering
large-scale images out of memory.
Traditional image registration techniques in ITK and other libraries (Elastix, ANTs) require
in-memory processing, meaning those techniques load images fully in memory (RAM) during
registration execution. Meanwhile, large image datasets can occupy terabytes of data for a single
image on the cloud and far exceed available memory. itk-dreg addresses this issue as a
map-reduce problem where images are successivly subdivided into subimages, registered,
and then composed into a descriptive output. Multiple itk-dreg registration graphs may be
executed in succession to yield a pipeline for multiresolution image registration.
itk-dreg provides three major components:

Concepts to describe the map-reduce registration problem;
A user-ready registration method to produce itk.DisplacementFieldTransforms from out-of-memory
registration with Dask scheduling and ITKElastix registration;
A developer framework to extend itk-dreg with novel registration and reduction methods.

Getting Started
To use itk-dreg, clone the Git repository and install with flit.
> python -m pip install flit
> git clone https://www.github.com/InsightSoftwareConsortium/itk-dreg.git
> cd itk-dreg/src
itk-dreg/src > python -m flit install

Several Jupyter Notebook examples are available for getting started. To run locally:
itk-dreg/src > python -m flit install --extras
itk-dreg/src > cd ../examples
itk-dreg/examples > jupyter notebook

Use Instructions
itk_dreg provides a framework to register a moving image onto a fixed image.
The output of a single run is an itk.Transform object that can be used
to resample the moving image onto the fixed image. Multiple runs can be chained
to successively refine registration over multiple image resolutions and over
various registration and reduction methods.
Use itk_dreg.register.register_images to assemble and run a task graph for distributed registration.
my_initial_transform = ...

# registration method returns an update to the initial transform

my_registration_schedule = itk_dreg.register_images(
fixed_chunk_size=(x,y,z),
initial_transform=my_initial_transform,
fixed_reader_ctor=my_construct_streaming_reader_method,
moving_reader_ctor=my_construct_streaming_reader_method,
block_registration_method=my_block_pair_registration_method_subclass,
reduce_method=my_postprocess_registration_method_subclass,
overlap_factors=[0.1,0.1,0.1]
)
my_result = my_registration_schedule.registration_result.compute()

final_transform = itk.CompositeTransform()
final_transform.append_transform(my_initial_transform)
final_transform.append_transform(my_result.transforms.transform)

# we can use the result transform to resample the moving image to fixed image space

interpolator = itk.LinearInterpolateImageFunction.New(my_moving_image)

my_warped_image = itk.resample_image_filter(
my_moving_image,
transform=final_transform,
interpolator=interpolator,
use_reference_image=True,
reference_image=my_fixed_image
)

Components
Core Components
itk_dreg provides the following core components:

itk_dreg.register defines scheduling infrastructure and the main entry point into the
itk_dreg registration framework.
itk_dreg.base defines common types and virtual interfaces for the itk_dreg registration framework.
Virtual interfaces in itk_dreg.base.registration_interface serve as an entry point for
contributors to write their own registration and reduction methods.
itk_dreg.block defines common methods to map between voxel and spatial subdomains.

These components must be installed to use the itk_dreg registration framework.
Extended Components
itk_dreg includes a few common implementations to get started with image registration.
These components act as extensions and are not necessarily required for running itk_dreg.

itk_dreg.itk provides ITK-based methods to aid in image streaming and dask chunk scheduling.
itk_dreg.elastix adapts the ITKElastix registration routines for distributed
registration in itk_dreg.
itk_dreg.reduce_dfield implements a transform-reduction method to estimate a single
itk.DeformationFieldTransform from block registration results in itk_dreg.
itk_dreg.mock provides mock implementations of common framework components for use in
testing and debugging.

Alternate registration and transform reduction modules may be available in the future
either as part of itk_dreg or via community distributions.
Contributing
Refer to Contributing documentation for getting started with itk-dreg development.
Please direct feature requests or bug reports to the itk-dreg GitHub Issues
board.
License
itk-dreg is distributed under the Apache-2.0 permissive license.
Questions and Queries
itk-dreg is part of the Insight Toolkit tools ecosystem for medical image processing. We encourage developers to
reach out to the ITK community with questions on the ITK Discourse forums. Those
interested in custom or commercial development should reach out to Kitware to learn more.
Acknowledgements
itk-dreg was developed in part by with support from:

NIH NIMH BRAIN Initiative under award 1RF1MH126732.