# Quantum optimization algorithms for CT image segmentation from X-ray data

arXiv: https://arxiv.org/abs/2306.05522 · HTML: https://mlsanity.com/paper/2306.05522/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2023.

## Built on by (most cited first, 4 of 4)

- [Quantum Annealing Feature Selection on Light-weight Medical Image Datasets](https://mlsanity.com/paper/2502.19201/) (2025)
- [Quantum Feature Pyramid Gating for Seismic Image Segmentation](https://mlsanity.com/paper/2605.15370/) (2026)
- [Quantum CT via Dynamic Interval Encoding and Prior-Balanced QUBO Reconstruction](https://mlsanity.com/paper/2606.24561/) (2026)
- [Superpixel-Based QUBO for Scalable Quantum-Enhanced Medical Image Segmentation](https://mlsanity.com/paper/2607.24288/) (2026)

## Abstract (arXiv)

Computed tomography (CT) is an important imaging technique used in medical analysis of the internal structure of the human body. Previously, image segmentation methods were required after acquiring reconstructed CT images to obtain segmented CT images which made it susceptible to errors from both reconstruction and segmentation algorithms. However, this paper introduces a new approach using an advanced quantum optimization algorithm called quadratic unconstrained binary optimization (QUBO). This algorithm enables acquisition of segmented CT images from X-ray projection data with minimized discrepancies between experimentally obtained sinograms and quantized sinograms derived from quantized segmented CT images using the Radon transform. This study utilized D-Wave's hybrid solver system for verification on real-world X-ray data.