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Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Framework for Fusion of Data- and Model-Based Approaches for Ultrasound Simulation

Authors : Christine Tanner, Rastislav Starkov, Michael Bajka, Orcun Goksel

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

Navigation, acquisition and interpretation of ultrasound (US) images relies on the skills and expertise of the performing physician. Virtual-reality based simulations offer a safe, flexible and standardized environment to train these skills. Simulations can be data-based by displaying a-priori acquired US volumes, or ray-tracing based by simulating the complex US interactions of a geometric model. Here we combine these two approaches as it is relatively easy to gather US images of normal background anatomy and attractive to cover the range of rare findings or particular clinical tasks with known ground truth geometric models. For seamless adaption and change of US content we further require stitching, texture synthesis and tissue deformation simulations. We test the proposed hybrid simulation method by replacing embryos within gestational sacs by ray-traced embryos, and by simulating an ectoptic pregnancy.

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Metadata
Title
Framework for Fusion of Data- and Model-Based Approaches for Ultrasound Simulation
Authors
Christine Tanner
Rastislav Starkov
Michael Bajka
Orcun Goksel
Copyright Year
2018
Book
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Print ISBN: 978-3-030-00936-6

Electronic ISBN: 978-3-030-00937-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00937-3_39

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