X-ray 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-volume, that separate rigid structures into distinct layers, leaving all deformable organs in one layer, such that the sum resembles the original input. Our proposed model is validaed on 6 clinical datasets (∼ 7200 X-ray images) in addition to 615 real chest X-ray images. Despite the challenging aspects of modeling such a highly ill-posed problem, exciting and encouraging results are obtained paving the path for further contributions in this direction.
CITATION STYLE
Albarqouni, S., Fotouhi, J., & Navab, N. (2017). X-ray in-depth decomposition: Revealing the latent structures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10435 LNCS, pp. 444–452). Springer Verlag. https://doi.org/10.1007/978-3-319-66179-7_51
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