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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Reconstructing Sinus Anatomy from Endoscopic Video – Towards a Radiation-Free Approach for Quantitative Longitudinal Assessment

verfasst von : Xingtong Liu, Maia Stiber, Jindan Huang, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Verlag: Springer International Publishing

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Abstract

Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understand the relationship between sinus anatomy and surgical outcomes. We present a patient-specific, learning-based method for 3D reconstruction of sinus surface anatomy directly and only from endoscopic videos. We demonstrate the effectiveness and accuracy of our method on in and ex vivo data where we compare to sparse reconstructions from Structure from Motion, dense reconstruction from COLMAP, and ground truth anatomy from CT. Our textured reconstructions are watertight and enable measurement of clinically relevant parameters in good agreement with CT. The source code is available at https://​github.​com/​lppllppl920/​DenseReconstruct​ion-Pytorch.

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Metadaten
Titel
Reconstructing Sinus Anatomy from Endoscopic Video – Towards a Radiation-Free Approach for Quantitative Longitudinal Assessment
verfasst von
Xingtong Liu
Maia Stiber
Jindan Huang
Masaru Ishii
Gregory D. Hager
Russell H. Taylor
Mathias Unberath
Copyright-Jahr
2020
Buch
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Print ISBN: 978-3-030-59715-3

Electronic ISBN: 978-3-030-59716-0

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-59716-0_1

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