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

dc.contributor.authorHenzler, Philippen_US
dc.contributor.authorRasche, Volkeren_US
dc.contributor.authorRopinski, Timoen_US
dc.contributor.authorRitschel, Tobiasen_US
dc.contributor.editorGutierrez, Diego and Sheffer, Allaen_US
dc.date.accessioned2018-04-14T18:24:57Z
dc.date.available2018-04-14T18:24:57Z
dc.date.issued2018
dc.description.abstractAs 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.en_US
dc.description.number2
dc.description.sectionheadersImage Magic
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13369
dc.identifier.issn1467-8659
dc.identifier.pages377-388
dc.identifier.urihttps://doi.org/10.1111/cgf.13369
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13369
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectDeep learning
dc.subjectVolume rendering
dc.subjectInverse rendering
dc.subjectConvolutional neural networks
dc.subjectTomography
dc.titleSingle-image Tomography: 3D Volumes from 2D Cranial X-Raysen_US
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