Dalca2019MedIA - probabilistic diffeomorphism

[YipengHu]

Benchmarking the selected configurations in:
Dalca, A.V., Balakrishnan, G., Guttag, J. and Sabuncu, M.R., 2019. Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Medical image analysis, 57, pp.226-236.

This is related to #3.

Summary:

• Tasks:
  Unsupervised algorithms
  Optional: surface-based registration (segmentation maps)

• Transformation:
  Stationary-ODE-based diffeomorphism by predicting velocity and integration using scaling-and-square integration (DVF in DeepReg).

• Network and loss:
  3D UNet starting with 32 filters; (although similar, maybe worth a re-implementation), outputting at 1/2 voxel size (equivalent to extract_levels: [3] in DeepReg)
  difference using reparameterisation to predict the Gaussian parameters for the probabilistic loss; and the loss is minimising the lower bound, resulting in an image data term and a regularising term - interesting to see the difference to intra-SSD and inter-NCC.

• Data:
  atlas-based registration, i.e. register each image to an atlas computed independently

• Metrics:
  Dice on warped segmentation maps
  Jacobian