A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2003.08502v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Reconstructing Sinus Anatomy from Endoscopic Video – Towards a Radiation-free Approach for Quantitative Longitudinal Assessment
[article]
<span title="2020-07-03">2020</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2020-03-18">2020</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.08502v1">arXiv:2003.08502v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ph4ftd7f7vh3rh2xj6ale5x2cu">fatcat:ph4ftd7f7vh3rh2xj6ale5x2cu</a> </span>
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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
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.08502v2">arXiv:2003.08502v2</a>
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... nstructions 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/DenseReconstruction-Pytorch.
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