S. Albarqouni, J. Fotouhi, N. Navab X-ray In-Depth Decomposition: Revealing The Latent Structures Accepted to Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Quebec, Canada, September 2017 A pre-print version is available online at arXiv. (bib)
	X-ray radiography is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics, and trauma procedures. Proper interpretation of the hidden and obscured anatomy in X-ray images remains a challenge and often requires high radiation dose and imaging from several perspectives. In this work, we aim at decomposing the conventional X-ray image into d X-ray components of independent, non-overlapped, clipped sub-volumes using deep learning approach. Despite the challenging aspects of modelling such a highly ill-posed problem, exciting and encouraging results are obtained paving the path for further contributions in this direction.
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