X-ray In-Depth Decomposition: Revealing The Latent Structures
This addresses the challenge of obscured anatomy in X-ray imaging for medical applications like diagnosis and surgery, though it appears incremental as it builds on existing deep learning approaches.
The paper tackles the problem of interpreting hidden anatomy in X-ray images by decomposing them into independent components using deep learning, achieving encouraging results that pave the way for future work.
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 modeling such a highly ill-posed problem, exciting and encouraging results are obtained paving the path for further contributions in this direction.