3d freehand ultrasound reconstruction by reference-based point cloud registration.

Purpose: This study aims to address the challenging estimation of trajectories from freehand ultrasound examinations by means of registration of automatically generated surface points. Current approaches to inter-sweep point cloud registration can be improved by incorporating heatmap predictions, but practical challenges such as label-sparsity or only partially overlapping coverage of target structures arise when applying realistic examination conditions.

Methods: We propose a pipeline comprising three stages: (1) Utilizing a Free Point Transformer for coarse pre-registration, (2) Introducing HeatReg for further refinement using support point clouds, and (3) Employing instance optimization to enhance predicted displacements.

ConvexAdam: Self-Configuring Dual-Optimisation-Based 3D Multitask Medical Image Registration.

Registration of medical image data requires methods that can align anatomical structures precisely while applying smooth and plausible transformations. Ideally, these methods should furthermore operate quickly and apply to a wide variety of tasks. Deep learning-based image registration methods usually entail an elaborate learning procedure with the need for extensive training data.