| Name | eReg JSON |
| Version |
0.0.10
JSON |
| download |
| home_page | https://github.com/BrainLesion/eReg |
| Summary | efficient, pythonic cross-platform image registrations |
| upload_time | 2024-04-16 21:09:49 |
| maintainer | None |
| docs_url | None |
| author | Sarthak Pati |
| requires_python | <4.0,>=3.8 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://pypi.python.org/pypi/eReg/)
[](http://ereg.readthedocs.io/?badge=latest)
[](https://github.com/BrainLesion/eReg/actions/workflows/tests.yml)
# eReg - A Simple Registration Tool
**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.
## Need
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.
## Installation
With a Python 3.8+ environment, you can install **eReg** from [pypi.org](https://pypi.org/project/eReg/).
1. Create a virtual environment
```sh
python3 -m venv venv_ereg ## using native python venv
# conda create -n venv_ereg python=3.8 ## using conda
```
2. Activate the virtual environment
```sh
source venv_ereg/bin/activate ## using native python venv
# conda activate venv_ereg ## using conda
```
3. Install eReg
```sh
pip install ereg
```
## Usage
**eReg** can be used via the command line or as a Python package.
### Command Line Interface
The command line interface is available via the `ereg` command:
```sh
(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.
```
### Pythonic Interface
The `ereg` package provides two Python interfaces, an object-oriented interface, as well as convenience functions. A [Jupyter notebook tutorial](https://github.com/BrainLesion/tutorials/tree/main/eReg) is available to illustrate usage of the Python API.
#### Object-Oriented Interface
The `register` method represents the core-of the object-oriented interface:
```python
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:
```python
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,
)
```
#### Functional Interface
Additionally, **eReg** provides functional wrappers for convenience.
```python
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
)
```
## Customization
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](https://github.com/BrainLesion/eReg/blob/main/ereg/configurations/sample_config.yaml). More details on the parameters and their options can be found in the configuration file itself.
## Extending eReg
To extend eReg, you first need to install **eReg** from source. Clone the repository and install the package:
```sh
git clone https://github.com/BrainLesion/eReg.git
cd eReg
pip install -e .
```
<!-- ## Citation TODO -->
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"description": "[](https://pypi.python.org/pypi/eReg/)\n[](http://ereg.readthedocs.io/?badge=latest)\n[](https://github.com/BrainLesion/eReg/actions/workflows/tests.yml)\n\n# eReg - A Simple Registration Tool\n**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.\n\n## Need\nBecause 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.\n\n\n\n## Installation\n\nWith a Python 3.8+ environment, you can install **eReg** from [pypi.org](https://pypi.org/project/eReg/).\n\n1. Create a virtual environment\n\n```sh\npython3 -m venv venv_ereg ## using native python venv\n# conda create -n venv_ereg python=3.8 ## using conda\n```\n\n2. Activate the virtual environment\n\n```sh\nsource venv_ereg/bin/activate ## using native python venv\n# conda activate venv_ereg ## using conda\n```\n\n3. Install eReg\n\n```sh\npip install ereg\n```\n\n## Usage\n\n**eReg** can be used via the command line or as a Python package. \n\n### Command Line Interface\n\nThe command line interface is available via the `ereg` command:\n\n```sh\n(venv_ereg) ~> ereg -h\nusage: eReg version0.0.4.post76.dev0+0d89ce7 [-h] -m -t -o -c [-tff] [-lf] [-gt]\n\nSimple registration.\n\noptions:\n -h, --help show this help message and exit\n -m , --movingImg The moving image to register. Can be comma-separated list of images or directory of images.\n -t , --targetImg The target image to register to.\n -o , --output The output. Can be single file or a directory.\n -c , --config The configuration file to use.\n -tff , --transfile Registration transform file; if provided, will use this transform instead of computing a new one or will save. Defaults to None.\n -lf , --log_file The log file to write to. Defaults to None.\n -gt , --gt The ground truth image.\n```\n\n### Pythonic Interface\nThe `ereg` package provides two Python interfaces, an object-oriented interface, as well as convenience functions. A [Jupyter notebook tutorial](https://github.com/BrainLesion/tutorials/tree/main/eReg) is available to illustrate usage of the Python API.\n\n#### Object-Oriented Interface\n\nThe `register` method represents the core-of the object-oriented interface:\n\n```python\nfrom ereg.registration import RegistrationClass\n\nregistration_obj = RegistrationClass(configuration_file) # the configuration file to use to customize the registration, and is optional\nregistration_obj.register(\n target_image=target_image_file, # the target image, which can be either a file or SimpleITK.Image object\n moving_image=moving_image_file, # the moving image, which can be either a file or SimpleITK.Image object\n output_image=output_file, # the output image to save the registered image to\n 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\n log_file=log_file, # the log file to write to\n)\n```\n\nFurther, a resample method is available to use previously computed transforms to resample a moving image:\n\n```python\nregistration_obj.resample_image(\n target_image=target_image_file,\n moving_image=moving_image_file,\n output_image=output_file,\n transform_file=transform_file,\n log_file=log_file,\n)\n```\n\n\n#### Functional Interface\n\nAdditionally, **eReg** provides functional wrappers for convenience.\n\n```python\nfrom ereg import registration_function\n\nssim = registration_function(\n target_image=target_image_file, # the target image, which can be either a file or SimpleITK.Image object\n moving_image=moving_image_file, # the moving image, which can be either a file or SimpleITK.Image object\n output_image=output_file, # the output image to save the registered image to\n 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\n log_file=log_file, # the log file to write to\n configuration=configuration_file, # the configuration file to use to customize the registration, and is optional\n)\n```\n\n## Customization\n\neReg'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](https://github.com/BrainLesion/eReg/blob/main/ereg/configurations/sample_config.yaml). More details on the parameters and their options can be found in the configuration file itself.\n\n\n## Extending eReg\n\nTo extend eReg, you first need to install **eReg** from source. Clone the repository and install the package:\n\n```sh\ngit clone https://github.com/BrainLesion/eReg.git\ncd eReg\npip install -e .\n```\n\n<!-- ## Citation TODO -->\n\n",
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