Driving Points Prediction For Abdominal Probabilistic Registration

TL;DR

This paper introduces an end-to-end learned driving points predictor to enhance probabilistic abdominal registration, significantly improving registration accuracy across multiple datasets and models.

Contribution

It proposes a novel end-to-end training approach for selecting driving points, tailored to specific registration pipelines, outperforming standard methods.

Findings

Improved registration performance in 11 out of 12 experiments.

Enhanced robustness and accuracy of probabilistic displacement models.

Effective across different datasets and modalities.

Abstract

Inter-patient abdominal registration has various applications, from pharmakinematic studies to anatomy modeling. Yet, it remains a challenging application due to the morphological heterogeneity and variability of the human abdomen. Among the various registration methods proposed for this task, probabilistic displacement registration models estimate displacement distribution for a subset of points by comparing feature vectors of points from the two images. These probabilistic models are informative and robust while allowing large displacements by design. As the displacement distributions are typically estimated on a subset of points (which we refer to as driving points), due to computational requirements, we propose in this work to learn a driving points predictor. Compared to previously proposed methods, the driving points predictor is optimized in an end-to-end fashion to infer driving points tailored for a specific registration pipeline. We evaluate the impact of our contribution on two different datasets corresponding to different modalities. Specifically, we compared the performances of 6 different probabilistic displacement registration models when using a driving points predictor or one of 2 other standard driving points selection methods. The proposed method improved performances in 11 out of 12 experiments.