Department of Translational Surgical Oncology (TSO)

Augmented Reality visualization by superimposing virtual images: Tumors (yellow), Vascular Tree (blue), Liver (red).

The interdisciplinary department "Translational Surgical Oncology (TSO)" conducts research on computer- and robot-assisted surgical systems at the intersection of engineering, computer science, and medicine. Our overarching goal is to optimize therapy along the surgical treatment pathway and improve patient outcomes through the integration of artificial intelligence, robotics, and sensor technology. The key objective is to quantify surgical skills and expertise using Surgical Data Science, robotics, and novel sensor systems, and to transfer this knowledge into intelligent machine systems. Our research is centered around three essential pillars that aim to advance the next generation of robotic surgery: machine learning methods for video-based workflow analysis, soft tissue navigation, and the semi-automatic execution of robot-assisted surgical procedures. To address the current limitation in available data, we are developing techniques for the synthetic generation and simulation of realistic training datasets. Additionally, we conduct proof-of-concept studies to ensure the clinical applicability and translation of our research.

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Prof. Dr. Stefanie Speidel
Translational Surgical Oncology
Phone: +49 (0)351 458 5413
E-Mail: Stefanie.speidel(at)nct-dresden.de

Rivoir, D., Funke, I., & Speidel, S. (2024). On the pitfalls of batch normalization for end-to-end video learning: A study on surgical workflow analysis. Medical Image Analysis, 103126.
Kolbinger, F. R., Rinner, F. M., Jenke, A. C., Carstens, M., Krell, S., Leger, S., ..., Speidel, S., & Bodenstedt, S. (2023). Anatomy segmentation in laparoscopic surgery: comparison of machine learning and human expertise–an experimental study. International Journal of Surgery, 109(10), 2962-2974.
Wagner, M., Müller-Stich, B. P., Kisilenko, A., Tran, D., Heger, P., Mündermann, L., ... & Speidel, S., Bodenstedt, S. (2023). Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark. Medical image analysis, 86, 102770.
Bhasker, N., Kolbinger, F. R., Skorobohach, N., Zwanenburg, A., Löck, S., Weitz, J., ... & Kühn, J. P. (2023). Prediction of clinically relevant postoperative pancreatic fistula using radiomic features and preoperative data. Scientific Reports, 13(1), 7506.
Kolbinger, F. R., Bodenstedt, S., Carstens, M., Leger, S., Krell, S., Rinner, F. M., ... & Speidel, S. (2023). Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study. European Journal of Surgical Oncology, 106996.

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