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Titel:

X-ray In-Depth Decomposition: Revealing The Latent Structures

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Albarqouni, S.; Fotouhi, J.; Navab, N.
Abstract:
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, cl...     »
Stichworte:
miccai,Xray,Depth,deeplearning
Kongress- / Buchtitel:
International Conference on Medical Image Computing and Computer-Assisted Intervention
Ausrichter der Konferenz:
Springer
Jahr:
2017
Seiten:
444--452
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