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About

Marcel Bengs is a researcher at the forefront of medical robotics and computer-assisted intervention, with a particular focus on enhancing surgical precision through advanced sensing and imaging. His primary research areas include force estimation in minimally invasive surgery, 4D spatio-temporal data analysis, and needle steering. Bengs’s most impactful work, "Deep learning with 4D spatio-temporal data representations for OCT-based force estimation" (2020, 27 citations), pioneers the use of deep learning on volumetric optical coherence tomography data to estimate tool-tissue interaction forces—a critical capability for restoring haptic feedback in robotic surgery. He further advanced this field by investigating optical force estimation for soft tissue interactions (2023, 12 citations), directly addressing the major limitation of force feedback in robotic systems. Beyond force sensing, Bengs has contributed to synthesizing strategies for needle steering in gelatin phantoms (2020) and systematic analysis of volumetric ultrasound for markerless 4D motion tracking (2022), the latter having significant implications for motion compensation in radiation therapy. His work on deep learning-based EEG electrode detection (2019) also demonstrates versatility in biomedical signal processing. With a growing citation impact, Bengs is establishing himself as a key innovator in image-guided interventions and robotic surgery.