The image-to-physical liver registration sparse data challenge: characterizing inverse biomechanical model resolution

Jon S. Heiselman; Michael I. Miga

doi:10.1117/12.2550535

16 March 2020 The image-to-physical liver registration sparse data challenge: characterizing inverse biomechanical model resolution

Jon S. Heiselman, Michael I. Miga

Author Affiliations +

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113151F (2020) https://doi.org/10.1117/12.2550535
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Image-guided liver surgery relies on intraoperatively acquired data to create an accurate alignment between image space and the physical patient anatomy. Often, sparse data of the anterior liver surface can be collected for these registrations. However, achieving accurate registration to sparse surface data when soft tissue deformation is present remains a challenging open problem. While many approaches have been developed, a common standard for comparing algorithm performance has yet to be adopted. The image-to-physical liver registration sparse data challenge offers a publicly available dataset of realistic sparse data patterns collected on a deforming liver phantom for the purpose of evaluating and comparing potential registration approaches. Additionally, the challenge is designed to allow testing and characterization of these methods as a general utility for the registration community. Using this challenge environment, an inverse biomechanical method for deformable registration to sparse data was investigated with respect to how whole-organ target registration error (TRE) is impacted by a model parameter that controls the spatial reconstructive resolution of mechanical loads applied to the organ. For this analysis, this resolution parameter was varied across a wide range of values and TRE was calculated from the challenge dataset. An optimal parameter value for model resolution was found and average TRE across the 112 sparse data challenge cases was reduced to 3.08 ± 0.85 mm, an approximate 32% improvement over previously reported results. The value of the data offered by the sparse data challenge is evident. This work was performed entirely using information automatically generated by the challenge submission and processing site.

Conference Presentation

Citation Download Citation

Jon S. Heiselman and Michael I. Miga "The image-to-physical liver registration sparse data challenge: characterizing inverse biomechanical model resolution", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151F (16 March 2020); https://doi.org/10.1117/12.2550535

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