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Physics-based Deep Neural Network for Augmented Reality during Liver Surgery
Abstract : In this paper we present an approach combining a finite element method and a deep neural network to learn complex elastic deformations with the objective of providing augmented reality during hep-atic surgery. Derived from the U-Net architecture, our network is built entirely from physically-based simulations of a preoperative segmenta-tion of the organ. These simulations are performed using an immersed-boundary method, which offers several numerical and practical benefits, such as not requiring boundary-conforming volume elements. We perform a quantitative assessment of the method using synthetic and ex vivo patient data. Results show that the network is capable of solving the deformed state of the organ using only a sparse partial surface displacement data and achieve similar accuracy as a FEM solution, while being about 100x faster. When applied to an ex vivo liver example, we achieve the registration in only 3 ms with a mean target registration error (TRE) of 2.9 mm.
Cited literature [21 references]
https://hal.inria.fr/hal-02158862
Contributor : Andrea Mendizabal Connect in order to contact the contributor
Submitted on : Wednesday, June 19, 2019 - 4:27:06 PM
Last modification on : Friday, February 4, 2022 - 3:16:19 AM
Contributor : Andrea Mendizabal Connect in order to contact the contributor
Submitted on : Wednesday, June 19, 2019 - 4:27:06 PM
Last modification on : Friday, February 4, 2022 - 3:16:19 AM
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MICCAI2019(4).pdf
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