Single-image Tomography: 3D Volumes from 2D Cranial X-Rays

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Date
2018
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Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
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.
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@article{
10.1111:cgf.13369
, journal = {Computer Graphics Forum}, title = {{
Single-image Tomography: 3D Volumes from 2D Cranial X-Rays
}}, author = {
Henzler, Philipp
and
Rasche, Volker
and
Ropinski, Timo
and
Ritschel, Tobias
}, year = {
2018
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, ISSN = {
1467-8659
}, DOI = {
10.1111/cgf.13369
} }
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