# Synthetic X‑ray‑driven tracking and control of miniature medical devices

## TL;DR

This paper introduces a new framework to generate synthetic X-ray images for controlling tiny medical devices during surgery, improving precision and reducing errors.

## Contribution

The novel contribution is a framework called MicroSyn-X that generates high-quality, auto-labeled synthetic X-ray data for training robotic systems.

## Key findings

- MicroSyn-X enables real-time tracking and control of miniature medical devices in complex environments.
- The framework improves robustness under low contrast, high noise, and occlusion conditions typical in surgical X-rays.
- An open-source X-ray dataset for miniature medical devices is provided to support future benchmarking.

## Abstract

The clinical translation of miniature medical devices (MMDs) for minimally invasive surgery promises transformative advances in biomedical engineering, offering enhanced precision, reduced patient trauma and faster recovery times. However, their effective deployment in complex anatomies under real-time X-ray guidance—a widely used surgical imaging modality—presents challenges such as low imaging quality and difficulties of spatial MMD control. Manual identification and operation are labour intensive and error prone. Meanwhile, deep learning-based automation is limited by the scarcity of annotated X-ray datasets of MMDs owing to costly data collection, laborious annotation and privacy constraints. Here we introduce MicroSyn-X, a framework for training computer vision models to enable robotic teleoperation of MMDs using synthesized high-fidelity, pixel-accurate, auto-labelled and domain-randomized X-ray images, eliminating manual data curation. Integrating MicroSyn-X into a teleoperated robotic system enables real-time localization and navigation of magnetic soft and magnetic liquid MMDs within both ex vivo and dynamic in vivo environments, demonstrating robustness under challenging imaging conditions of low contrast, high noise and occlusion. With these promises, we open source the X-ray MMD dataset to enable benchmarking. Addressing data scarcity and enabling real-time robotic navigation, this work advances MMD-assisted minimally invasive surgery towards next-generation precision interventions.

Wang et al. introduce MicroSyn-X, a synthetic X-ray data generation framework that overcomes data scarcity in miniature medical devices, enabling robust deep learning-based tracking and real-time robotic navigation in challenging surgical settings.