Displacement Representation for Conditional Point Cloud Registration

In this work, we create a point cloud-based framework based on Free Point Transformers (FPTs) for 2D/3D registration of untracked ultrasound (US) sweeps. Applications include outpatient follow-up assessments and intraoperative scenarios like ultrasound-guided navigation. Through a simple modification in displacement prediction representation, we enhance registration results by more than 25% w.r.t. prior work while preserving the model-free paradigm, maintaining network parameters, and only marginally increasing computation time. Experiments on the SegThy dataset, featuring manually segmented anatomies on MR (magnetic resoncance) scans in the thyroid gland area, demonstrate our method’s effectiveness. We simulate numerous realistic ultrasound sweeps, aiming to register them back into the MR volume. Beyond methodological contributions, our fast registration framework strives to enable clinically capable systems, advancing ultrasound-guided surgery.