Metadata-Version: 2.1
Name: convexAdam
Version: 0.1
Summary: Convex Adam
Home-page: https://github.com/multimodallearning/convexAdam
Author: Mattias Paul Heinrich
Author-email: heinrich@imi.uni-luebeck.de
License: Apache 2.0
Project-URL: Bug Tracker, https://github.com/multimodallearning/convexAdam/issues
Platform: unix
Platform: linux
Platform: osx
Platform: cygwin
Platform: win32
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: nibabel
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: SimpleITK
Requires-Dist: torch
Provides-Extra: testing
Requires-Dist: pytest >=6.0 ; extra == 'testing'
Requires-Dist: pytest-cov >=2.0 ; extra == 'testing'
Requires-Dist: mypy >=0.910 ; extra == 'testing'
Requires-Dist: flake8 >=3.9 ; extra == 'testing'
Requires-Dist: tox >=3.24 ; extra == 'testing'

# convexAdam

Learn2Reg 2021 Submission
## Fast and accurate optimisation for registration with little learning

![Slide1](images/L2R_2021_ConvexAdam.002.jpeg?raw=true "Coupled Convex")
![Slide2](images/L2R_2021_ConvexAdam.003.jpeg?raw=true "Coupled Convex")
![Slide3](images/L2R_2021_ConvexAdam.004.jpeg?raw=true "Coupled Convex")

Please see details in our paper and if you use the code, please cite the following:
Siebert, H., Hansen, L., Heinrich, M.P. (2022). Fast 3D Registration with Accurate Optimisation and Little Learning for Learn2Reg 2021. In: Aubreville, M., Zimmerer, D., Heinrich, M. (eds) Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. MICCAI 2021. Lecture Notes in Computer Science(), vol 13166. Springer, Cham. https://doi.org/10.1007/978-3-030-97281-3_25

and

Heinrich, M.P., Papież, B.W., Schnabel, J.A., Handels, H. (2014). Non-parametric Discrete Registration with Convex Optimisation. In: Ourselin, S., Modat, M. (eds) Biomedical Image Registration. WBIR 2014. Lecture Notes in Computer Science, vol 8545. Springer, Cham. https://doi.org/10.1007/978-3-319-08554-8_6



## Excellent results on Learn2Reg 2021 challenge
- for multimodal CT/MR registration (Task1) 
- intra-patient lung CT alignment (Task2)
- and inter-patient whole brain MRI deformations (Task3)
[Challenge Website](https://learn2reg.grand-challenge.org)

![Slide4](images/L2R_2021_ConvexAdam.005.jpeg?raw=true "Coupled Convex")

![Results](images/l2r2021_convexAdam.png?raw=true "Results")
