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

arXiv: https://arxiv.org/abs/1612.06096 · HTML: https://mlsanity.com/paper/1612.06096/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2016.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021)
- [GAN-based disentanglement learning for chest X-ray rib suppression](https://mlsanity.com/paper/2110.09134/) (2021)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue](https://mlsanity.com/paper/2102.12960/) (2021)
- [The Impact of Loss Functions and Scene Representations for 3D/2D Registration on Single-view Fluoroscopic X-ray Pose Estimation](https://mlsanity.com/paper/2308.00214/) (2023)

## Abstract (arXiv)

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.