Deep-Learning-Based CT Lung Registration

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About

Editors:
User Mugshot AHering  User Mugshot BramVanGinneken  User Mugshot nlessmann  User Mugshot kiranvaidhya 
Contact email:
alessa.hering@mevis.fraunhofer.de
Image Version:
fb3bf078-ba1e-417b-a138-58745e96c4cc — Sept. 3, 2021
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.

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

Interfaces

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

Inputs Outputs
1
    Fixed Image
    Moving Image
    Fixed Mask
    Moving Mask
    Fixed Image
    Warped Image
    Displacement Field

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