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Robust and Scalable Interactive Freeform Modeling of High Definition Medical Images

Abstract : Whole-body anatomically correct high-resolution 3D medical images are instrumental for physical simulations. Unfortunately, only a limited number of acquired datasets are available and the scope of possible applications is limited by the patient's posture. In this paper, we propose an extension of the interactive cage-based deformation pipeline VoxMorph [1], for labeled voxel grids allowing to eciently explore the space of plausible poses while preserving the tissues' internal structure. We propose 3 main contributions to overcome the limitations of this pipeline: (i) we improve the robustness by proposing a deformation diusion scheme, (ii) we improve the accuracy by proposing a new error-metric for the renement process of the motion adaptive structure, (iii) we improve the scalability by proposing an out-of-core implementation. Our method is easy to use for novice users, robust and scales up to 3D images that do not t in memory, while oering limited distortion and mass loss. We evaluate our approach on postured whole-body segmented images and present an electro-magnetic wave exposure study for human-waves interaction simulations.
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Submitted on : Monday, February 16, 2015 - 2:34:40 PM
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  • HAL Id : hal-01117140, version 1


Noura Faraj, Jean-Marc Thiery, Isabelle Bloch, Nadège Varsier, Joe Wiart, et al.. Robust and Scalable Interactive Freeform Modeling of High Definition Medical Images. MICCAI Workshop on Mesh Processing in Medical Imaging, Oct 2012, Nice, France. pp.1-11. ⟨hal-01117140⟩



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