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Machine learning-based approaches outperform competing methods in mostdisciplines relevant to diagnostic radiology. Interventional radiology,however, has not yet benefited substantially from the advent of deep learning,in particular because of two reasons: 1) Most images acquired during theprocedure are never archived and are thus not available for learning, and 2)even if they were available, annotations would be a severe challenge due to thevast amounts of data. When considering fluoroscopy-guided procedures, aninteresting alternative to true interventional fluoroscopy is in silicosimulation of the procedure from 3D diagnostic CT. In this case, labeling iscomparably easy and potentially readily available, yet, the appropriateness ofresulting synthetic data is dependent on the forward model. In this work, wepropose DeepDRR, a framework for fast and realistic simulation of fluoroscopyand digital radiography from CT scans, tightly integrated with the softwareplatforms native to deep learning. We use machine learning for materialdecomposition and scatter estimation in 3D and 2D, respectively, combined withanalytic forward projection and noise injection to achieve the requiredperformance. On the example of anatomical landmark detection in X-ray images ofthe pelvis, we demonstrate that machine learning models trained on DeepDRRsgeneralize to unseen clinically acquired data without the need for re-trainingor domain adaptation. Our results are promising and promote the establishmentof machine learning in fluoroscopy-guided procedures.

DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures