Department of Translational Surgical Oncology

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 interface of engineering, computer science, and medicine. The goal of our research is to quantify surgical skills and expertise through the use of artificial intelligence (AI), robotics, and sensors, and to augment these capabilities with intelligent assistance systems. The research and methodological focus is on machine learning methods in the field of video-based workflow analysis, soft-tissue navigation, and shared autonomy for robot-assisted surgery. Our research is complemented by cross-cutting topics such as synthetic data generation and proof-of-concept studies to enable translation into clinical practice.

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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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