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3D Probabilistic Segmentation and Volumetry from 2D projection images

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Document pages: 10 pages

Abstract: X-Ray imaging is quick, cheap and useful for front-line care assessment andintra-operative real-time imaging (e.g., C-Arm Fluoroscopy). However, itsuffers from projective information loss and lacks vital volumetric informationon which many essential diagnostic biomarkers are based on. In this paper weexplore probabilistic methods to reconstruct 3D volumetric images from 2Dimaging modalities and measure the models performance and confidence. We showour models performance on large connected structures and we test forlimitations regarding fine structures and image domain sensitivity. We utilizefast end-to-end training of a 2D-3D convolutional networks, evaluate our methodon 117 CT scans segmenting 3D structures from digitally reconstructedradiographs (DRRs) with a Dice score of $0.91 pm 0.0013$. Source code will bemade available by the time of the conference.

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