An Adaptive Correspondence Scoring Framework for Unsupervised Image
  Registration of Medical Images
Xiaoran Zhang John C. Stendahl Lawrence Staib Albert J. Sinusas Alex Wong James S. Duncan
We propose an adaptive training scheme for unsupervised medical image
registration. Existing methods rely on image reconstruction as the primary
supervision signal. However, nuisance variables (e.g. noise and covisibility)
often cause the loss of correspondence between medical images, violating the
Lambertian assumption in physical waves (e.g. ultrasound) and consistent
imaging acquisition. As the unsupervised learning scheme relies on intensity
constancy to establish correspondence between images for reconstruction, this
introduces spurious error residuals that are not modeled by the typical
training objective. To mitigate this, we propose an adaptive framework that
re-weights the error residuals with a correspondence scoring map during
training, preventing the parametric displacement estimator from drifting away
due to noisy gradients, which leads to performance degradations. To illustrate
the versatility and effectiveness of our method, we tested our framework on
three representative registration architectures across three medical image
datasets along with other baselines. Our proposed adaptive framework
consistently outperforms other methods both quantitatively and qualitatively.
Paired t-tests show that our improvements are statistically significant. The
code will be publicly available at
\url{https://voldemort108x.github.io/AdaCS/}.