Deep-Learning-Based CT Lung Registration


Logo for Deep-Learning-Based CT Lung Registration

About

Creators:
Image Version:
fb3bf078-ba1e-417b-a138-58745e96c4cc
Last updated:
Sept. 3, 2021, 7:01 a.m.
Associated publication:
Hering A, Häger S, Moltz J, Lessmann N, Heldmann S, van Ginneken B. CNN-based lung CT registration with multiple anatomical constraints. Medical Image Analysis. 2021;72:102139.

Interfaces

This algorithm implements all of the following input-output combinations:

Inputs Outputs
1
  • Fixed Image (Image)
  • Moving Image (Image)
  • Fixed Mask (Segmentation)
  • Moving Mask (Segmentation)
  • Fixed Image (Image)
  • Warped Image (Image)
  • Displacement Field (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