Multi-Objective Learning for Deformable Image Registration
Monika Grewal Henrike Westerveld Peter A. N. Bosman Tanja Alderliesten
Deformable image registration (DIR) involves optimization of multiple
conflicting objectives, however, not many existing DIR algorithms are
multi-objective (MO). Further, while there has been progress in the design of
deep learning algorithms for DIR, there is no work in the direction of MO DIR
using deep learning. In this paper, we fill this gap by combining a recently
proposed approach for MO training of neural networks with a well-known deep
neural network for DIR and create a deep learning based MO DIR approach. We
evaluate the proposed approach for DIR of pelvic magnetic resonance imaging
(MRI) scans. We experimentally demonstrate that the proposed MO DIR approach --
providing multiple registration outputs for each patient that each correspond
to a different trade-off between the objectives -- has additional desirable
properties from a clinical use point-of-view as compared to providing a single
DIR output. The experiments also show that the proposed MO DIR approach
provides a better spread of DIR outputs across the entire trade-off front than
simply training multiple neural networks with weights for each objective
sampled from a grid of possible values.