Package that performs affine and LDDMM registration easily
The recommended way to use this package is to install Docker. Docker is currently available on Mac OS X El Capitan 10.11 and newer macOS releases, the following Ubuntu versions: Zesty 17.04 (LTS), Yakkety 16.10, Xenial 16.04 (LTS), Trusty 14.04 (LTS), and Windows 10.
The only software dependency needed if using the recommended method is Docker. The following dependencies are included in the Docker Image.
External libraries:
- Insight Segmentation and Registration Toolkit (ITK) -- 4.12.2
Python depedencies:
- jupyter -- (1.0.0)
- numpy -- (1.13.3)
- scikit-image -- (0.13.1)
- scikit-learn -- (0.19.1)
- scipy -- (1.0.0)
- SimpleITK -- (1.0.1)
We have tested the Docker image and build on macOS High Sierra (on MacBook Pro with 2.9 GHz Intel Core i7 and 16 GB RAM) and Ubuntu Xenial 16.04.3 LTS (with 64 GB RAM).
Once Docker is installed on your machine, pull the neurodata/ndreg image from Docker Hub here as follows:
docker pull neurodata/ndreg
It will typically take around 3 minutes to pull the entire Docker image.
In order to use the functionality built into this Docker image, you need to run the Docker image:
docker run -p 8888:8888 neurodata/ndreg
This should print a link to the terminal console that looks like this:
http://0.0.0.0:8888/?token=SOME_TOKEN
Go to this link in your browser by copying and pasting it.
Next, click on ndreg_demo.ipynb. Once the notebook opens, you can run all cells by clicking on 'Cell' and then 'Run All'.
The expected run time for this demo is ~ 2 minutes.
The last 3 cells in the demo notebook display images that should look the same (both the LDDMM registered image and the displacement field warped image) and a mean squared error