Deformable Registration through Learning of Context-Specific Metric Aggregation
This work addresses the need for more adaptive and accurate image registration in medical imaging, though it is incremental as it builds on existing metrics with a novel learning-based aggregation method.
The authors tackled the problem of suboptimal and globally tuned similarity metrics in deformable image registration by proposing a weakly supervised learning algorithm that estimates optimal local weightings of conventional metrics based on semantic context, achieving improved registration accuracy on three challenging anatomical datasets.
We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.