Deep-Learning-Based CT Lung Registration
About
- Fixed Image (The target image for registration)
- Moving Image (The image that will be warped during registration to the fixed image)
- Fixed Mask (Mask of the locations in the fixed image to consider)
- Moving Mask (Mask of the locations in the moving image to consider)
- Fixed Image (The target image for registration)
- Warped Image (The moving image warped to the fixed image)
- Displacement Field (Displacement vectors to align the moving image to the fixed image)
Model Facts
Summary
This deep-learning-based lung registration developed in [1] uses multiple anatomical constraints to supervise the training. For preprocessing, the lung mask of the inspiration and expiration scan are required. The algorithms will crop the input images to the lung regions and outputs:
cropped fixed image
warped moving image
displacement field in mm
For further questions or comments please contact us at alessa.hering@mevis.fraunhofer.de
[1] Hering, A., Häger, S., Moltz, J., Lessmann, N., Heldmann, S., & van Ginneken, B. (2021). CNN-based Lung CT Registration with Multiple Anatomical Constraints. Medical Image Analysis, 102139.
Mechanism
Validation and Performance
Uses and Directions
This algorithm was developed for research purposes only.
Warnings
Common Error Messages
Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3