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Single-image Tomography: 3D Volumes from 2D Cranial X-Rays
Ulm University, Ulm, Germany.
Ulm University, Ulm, Germany.
Ulm University, Ulm, Germany.ORCID iD: 0000-0002-7857-5512
University College London, London, United Kingdom.
2018 (English)In: Computer Graphics Forum (Proceedings of Eurographics 2018), Vol. 37, no 2, p. 377-388Article in journal (Refereed) Published
Abstract [en]

As many different 3D volumes could produce the same 2D x‐ray image, inverting this process is challenging. We show that recent deep learning‐based convolutional neural networks can solve this task. As the main challenge in learning is the sheer amount of data created when extending the 2D image into a 3D volume, we suggest firstly to learn a coarse, fixed‐resolution volume which is then fused in a second step with the input x‐ray into a high‐resolution volume. To train and validate our approach we introduce a new dataset that comprises of close to half a million computer‐simulated 2D x‐ray images of 3D volumes scanned from 175 mammalian species. Future applications of our approach include stereoscopic rendering of legacy x‐ray images, re‐rendering of x‐rays including changes of illumination, view pose or geometry. Our evaluation includes comparison to previous tomography work, previous learning methods using our data, a user study and application to a set of real x‐rays.

Place, publisher, year, edition, pages
Wiley-Blackwell Publishing Inc., 2018. Vol. 37, no 2, p. 377-388
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-152561DOI: 10.1111/cgf.13369ISI: 000434085600034Scopus ID: 2-s2.0-85051549169OAI: oai:DiVA.org:liu-152561DiVA, id: diva2:1261290
Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2018-11-14Bibliographically approved

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Ropinski, Timo

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  • Other style
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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