Pose-independent surface matching for intra-operative soft-tissue marker-less registration

Abstract

One of the main challenges in computer-assisted soft tissue surgery is the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilities by observing beyond exposed tissue surfaces. A new approach to marker-less guidance involves capturing the intra-operative patient anatomy with a range image device and doing a shape-based registration. However, as the target organ is only partially visible, typically does not provide salient features and underlies severe non-rigid deformations, surface matching in this context is extremely challenging. Furthermore, the intra-operatively acquired surface data may be subject to severe systematic errors and noise. To address these issues, we propose a new approach to establishing surface correspondences, which can be used to initialize fine surface matching algorithms in the context of intra-operative shape-based registration. Our method does not require any prior knowledge on the relative poses of the input surfaces to each other, does not rely on the detection of prominent surface features, is robust to noise and can be used for overlapping surfaces. It takes into account (1) similarity of feature descriptors, (2) compatibility of multiple correspondence pairs, as well as (3) the spatial configuration of the entire correspondence set. We evaluate the algorithm on time-of-flight (ToF) data from porcine livers in a respiratory liver motion simulator. In all our experiments the alignment computed from the established surface correspondences yields a registration error below 1cm and is thus well suited for initializing fine surface matching algorithms for intra-operative soft-tissue registration.