eReg is a robust, fast and user-friendly registration tool that can be used in clinical environments without the need for virtualization or containerization technologies. It supports most platforms across various hardware configurations.
Because of security concerns, users in clinical environments do not have access to virtualization and containerization technologies such as Docker and Singularity. This becomes a problem, because most research code (especially for image registration) is built around the need to have access to these technologies. Alternatively, some tools only work on a Linux environment, or they need specific hardware resources (such as a DL accelerator card), which are not always available in clinical settings.
With a Python 3.8+ environment, you can install eReg from pypi.org.
- Create a virtual environment
python3 -m venv venv_ereg ## using native python venv
# conda create -n venv_ereg python=3.8 ## using conda
- Activate the virtual environment
source venv_ereg/bin/activate ## using native python venv
# conda activate venv_ereg ## using conda
- Install eReg
pip install ereg
eReg can be used via the command line or as a Python package.
The command line interface is available via the ereg
command:
(venv_ereg) ~> ereg -h
usage: eReg version0.0.4.post76.dev0+0d89ce7 [-h] -m -t -o -c [-tff] [-lf] [-gt]
Simple registration.
options:
-h, --help show this help message and exit
-m , --movingImg The moving image to register. Can be comma-separated list of images or directory of images.
-t , --targetImg The target image to register to.
-o , --output The output. Can be single file or a directory.
-c , --config The configuration file to use.
-tff , --transfile Registration transform file; if provided, will use this transform instead of computing a new one or will save. Defaults to None.
-lf , --log_file The log file to write to. Defaults to None.
-gt , --gt The ground truth image.
The ereg
package provides two Python interfaces, an object-oriented interface, as well as convenience functions. A Jupyter notebook tutorial is available to illustrate usage of the Python API.
The register
method represents the core-of the object-oriented interface:
from ereg.registration import RegistrationClass
registration_obj = RegistrationClass(configuration_file) # the configuration file to use to customize the registration, and is optional
registration_obj.register(
target_image=target_image_file, # the target image, which can be either a file or SimpleITK.Image object
moving_image=moving_image_file, # the moving image, which can be either a file or SimpleITK.Image object
output_image=output_file, # the output image to save the registered image to
transform_file=transform_file, # the transform file to save the transform to; if already present, will use this transform instead of computing a new one
log_file=log_file, # the log file to write to
)
Further, a resample method is available to use previously computed transforms to resample a moving image:
registration_obj.resample_image(
target_image=target_image_file,
moving_image=moving_image_file,
output_image=output_file,
transform_file=transform_file,
log_file=log_file,
)
Additionally, eReg provides functional wrappers for convenience.
from ereg import registration_function
ssim = registration_function(
target_image=target_image_file, # the target image, which can be either a file or SimpleITK.Image object
moving_image=moving_image_file, # the moving image, which can be either a file or SimpleITK.Image object
output_image=output_file, # the output image to save the registered image to
transform_file=transform_file, # the transform file to save the transform to; if already present, will use this transform instead of computing a new one
log_file=log_file, # the log file to write to
configuration=configuration_file, # the configuration file to use to customize the registration, and is optional
)
eReg's registration and transformation parameters can be customized using a configuration file. The configuration file is a YAML file that contains the parameters for the registration. The default configuration file is present here. More details on the parameters and their options can be found in the configuration file itself.
To extend eReg, you first need to install eReg from source. Clone the repository and install the package:
git clone https://github.com/BrainLesion/eReg.git
cd eReg
pip install -e .