Research area

Interventional Imaging

Interventional imaging at the Albarqouni Lab, University of Bonn: low-dose X-ray guidance, catheter and device localisation, and AI for endoscopic and surgical video.

Interventional imaging is imaging during a procedure — the live X-ray that guides a catheter, the endoscope inside the colon, the camera of a surgical robot. Decisions are made in real time, the images are noisy and change from second to second, and every additional X-ray adds radiation dose for patients and staff. Our work brings AI to these settings, from interventional radiology to what is now often called surgical data science.

Low-dose X-ray guidance

With Siemens Healthineers we reduced the X-ray dose of interventional imaging without losing image quality: photon recycling and noise-model-driven denoising of ultra-low-dose fluoroscopy, low-rank denoising of digital subtraction angiography, and a real-time network trained on simulated low-dose images. Earlier work revealed hidden structures in single X-ray images through X-ray in-depth decomposition and single-view depth recovery for image-guided interventions.

Devices, catheters and stents

Knowing where devices are is essential during an intervention. We detected catheter electrodes and predicted their depth from a single view (CathNets), and segmented stents and multiple devices in fluoroscopy for endovascular aortic repair, and we co-edited the proceedings of the MICCAI workshops on intravascular imaging and computer-assisted stenting (CVII-STENT).

Endoscopic and surgical video

In endoscopy we took part in the Endoscopy Artefact Detection challenge and studied how artefacts such as blur, bubbles and specular reflections affect automated polyp detection — and how to make detectors robust to them. In surgical video, the SegSTRONG-C challenge (IEEE TMI 2026) benchmarked robust surgical tool segmentation under realistic corruptions such as smoke, bleeding and blur, and with Kyung Hee University our surgical tools project explored state-space vision models for robust instrument segmentation.

The common thread is robustness: models that keep working under the noise, artefacts and corruptions that are normal in the interventional suite and the operating room.

Research Collaboration and Funded Projects 2 filter all →

Publications 22 filter all →

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions--An EndoVis' 24 Challenge

Hao Ding, Yuqian Zhang, Tuxun Lu, Ruixing Liang, Hongchao Shu, Lalithkumar Seenivasan, Yonghao Long, Qi Dou, Cong Gao, Yicheng Leng, Seok Bong Yoo, Eung-Joo Lee, Zijian Wu, Yuxin Chen, Septimiu E. Salcudean, Samra Irshad, Shadi Albarqouni, Seong Tae Kim, Yueyi Sun, An Wang, Long Bai, Hongliang Ren, Ihsan Ullah, Ho-Gun Ha, Attaullah Khan, Hyunki Lee, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Sita Tailor, Ricardo Sanchez-Matilla, Imanol Luengo, Tianhao Fu, Jun Ma, Bo Wang, Marcos Fernández-Rodríguez, Estevao Lima, João L. Vilaça, Negin Ghamsarian, Klaus Schoeffmann, Raphael Sznitman, Mathias Unberath

IEEE Transactions on Medical Imaging, 2026

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

Sharib Ali, Felix Zhou, Barbara Braden, Adam A Bailey, Suhui Yang, Guanju Cheng, Pengyi Zhang, Xiaoqiong Li, Maxime Kayser, Roger D. Soberanis-Mukul, Shadi Albarqouni, Xiaokang Wang, Chunqing Wang, Seiryo Watanabe, İlkay Öksüz, Qingtian Ning, Shufan Yang, Mohammad Azam Khan, Xiaohong W. Gao, Stefano Realdon, Maxim V. Loshchenov, Julia A. Schnabel, James Edward East, Georges Wagnières, Victor B. Loschenov, Enrico Grisan, Christian Daul, Walter Blondel, Jens Rittscher

Scientific reports, 2020

Journal Q1IF 4.9 Interventional

All 22 publications →