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The ability to accurately account for intraoperative soft tissue deformations has been a longstanding barrier to efficacious translation of image-guided frameworks into abdominal interventions. In surgical applications, few data acquisition systems are amenable to stringent operative workflow constraints, and many are too costly for widespread adoption. Consequently, computational methods for surgical guidance based on sparse data obtained over the organ surface have become prevalent within approaches for image-to-patient alignment of soft tissue. However, the sparse data environment presents an especially challenging algorithmic landscape for accurately inferring deformable anatomical alignments between preoperative and intraoperative organ states from incomplete information sources. This work, presented as a preliminary conclusion to the image-to-physical liver registration sparse data challenge introduced at SPIE Medical Imaging 2019, seeks to characterize the potential for sparse data registration algorithms to achieve high fidelity predictions of intraoperative organ deformations from sparse descriptors of organ surface shape. A total of seven rigid and nonrigid biomechanical and deep learning registration techniques are compared, and the findings suggest that the family of biomechanically simulated boundary condition reconstruction techniques offers a promising opportunity for accurately estimating intraoperative organ deformation states from sparse intraoperative point clouds. Over a common dataset of 112 registration scenarios, this family of deformable registration techniques was found to outperform globally optimal rigid registrations, was robust to varying degrees of surface data coverage, and maintained good performance under added sources of measurement noise. Further analysis investigates error correlations among methods to illuminate sparse data performance within state-of-the-art image-to-physical registration algorithms.
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