Publikation
A systematic comparison of generative models for medical images
Hristina Uzunova; Matthias Wilms; Nils D Forkert; Heinz Handels; Jan Ehrhardt
In: International Journal of Computer Assisted Radiology and Surgery (IJCARS), Vol. 17, Pages 1-12, Springer, 2022.
Zusammenfassung
Purpose This work aims for a systematic comparison of popular shape and appearance models. Here, two statistical and four
deep-learning-based shape and appearance models are compared and evaluated in terms of their expressiveness described by
their generalization ability and specificity as well as further properties like input data format, interpretability and latent space
distribution and dimension.
Methods Classical shape models and their locality-based extension are considered next to autoencoders, variational
autoencoders, diffeomorphic autoencoders and generative adversarial networks. The approaches are evaluated in terms of
generalization ability, specificity and likeness depending on the amount of training data. Furthermore, various latent space
metrics are presented in order to capture further major characteristics of the models.
Results The experimental setup showed that locality statistical shape models yield best results in terms of generalization
ability for 2D and 3D shape modeling. However, the deep learning approaches show strongly improved specificity. In the
case of simultaneous shape and appearance modeling, the neural networks are able to generate more realistic and diverse
appearances. A major drawback of the deep-learning models is, however, their impaired interpretability and ambiguity of the
latent space.
Conclusions It can be concluded that for applications not requiring particularly good specificity, shape modeling can be
reliably established with locality-based statistical shape models, especially when it comes to 3D shapes. However, deep
learning approaches are more worthwhile in terms of appearance modeling.