# A Realistic Collimated X-Ray Image Simulation Pipeline

arXiv: https://arxiv.org/abs/2411.10308 · HTML: https://mlsanity.com/paper/2411.10308/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks](https://mlsanity.com/paper/1810.10863/) (2018)

## Built on by (most cited first, 1 of 1)

- [From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collimators via Hough Transform](https://mlsanity.com/paper/2509.04437/) (2025)

## Abstract (arXiv)

Collimator detection remains a challenging task in X-ray systems with unreliable or non-available information about the detectors position relative to the source. This paper presents a physically motivated image processing pipeline for simulating the characteristics of collimator shadows in X-ray images. By generating randomized labels for collimator shapes and locations, incorporating scattered radiation simulation, and including Poisson noise, the pipeline enables the expansion of limited datasets for training deep neural networks. We validate the proposed pipeline by a qualitative and quantitative comparison against real collimator shadows. Furthermore, it is demonstrated that utilizing simulated data within our deep learning framework not only serves as a suitable substitute for actual collimators but also enhances the generalization performance when applied to real-world data.