# Sebastian Bodenstedt

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/sebastian-bodenstedt/

## Facts

| Field | Value |
| --- | --- |
| Citations | 2,551 |
| Field | Surgical Simulation and Training |
| h-index | 26 |
| i10-index | 53 |
| Last Known Institution | German Cancer Research Center |
| OpenAlex ID | https://openalex.org/A5034437528 |
| ORCID iD | 0000-0002-2203-9729 |
| Works | 166 |

## Researcher papers

- [Machine Learning for Surgical Phase Recognition](https://scholariq.org/papers/machine-learning-for-surgical-phase-recognition/)
- [2018 Robotic Scene Segmentation Challenge](https://scholariq.org/papers/2018-robotic-scene-segmentation-challenge/)
- [Comparative Validation of Single-Shot Optical Techniques for Laparoscopic 3-D Surface Reconstruction](https://scholariq.org/papers/comparative-validation-of-single-shot-optical-techniques-for-laparoscopic-3-d/)
- [Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark](https://scholariq.org/papers/comparative-validation-of-machine-learning-algorithms-for-surgical-workflow-and/)
- [Dense<scp>GPU</scp>-enhanced surface reconstruction from stereo endoscopic images for intraoperative registration](https://scholariq.org/papers/dense-scp-gpu-scp-enhanced-surface-reconstruction-from-stereo-endoscopic-images/)
- [Can Masses of Non-Experts Train Highly Accurate Image Classifiers?](https://scholariq.org/papers/can-masses-of-non-experts-train-highly-accurate-image-classifiers/)
- [Physics‐based shape matching for intraoperative image guidance](https://scholariq.org/papers/physics-based-shape-matching-for-intraoperative-image-guidance/)
- [Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge](https://scholariq.org/papers/comparative-validation-of-multi-instance-instrument-segmentation-in-endoscopy/)
- [Artificial Intelligence-Assisted Surgery: Potential and Challenges](https://scholariq.org/papers/artificial-intelligence-assisted-surgery-potential-and-challenges/)
- [Development and validation of a sensor- and expert model-based training system for laparoscopic surgery: the iSurgeon](https://scholariq.org/papers/development-and-validation-of-a-sensor-and-expert-model-based-training-system/)
- [Can you feel the force just right? Tactile force feedback for training of minimally invasive surgery—evaluation of vibration feedback for adequate force application](https://scholariq.org/papers/can-you-feel-the-force-just-right-tactile-force-feedback-for-training-of/)

## Researcher topics

- [Surgical Simulation and Training](https://scholariq.org/topics/surgical-simulation-and-training/)
- [Soft Robotics and Applications](https://scholariq.org/topics/soft-robotics-and-applications/)
- [Anatomy and Medical Technology](https://scholariq.org/topics/anatomy-and-medical-technology/)
- [Augmented Reality Applications](https://scholariq.org/topics/augmented-reality-applications/)
- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)

## Researcher university

- [German Cancer Research Center](https://scholariq.org/institutions/german-cancer-research-center/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.