Browsing by Author "Bodenstedt, Sebastian"

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Open Access
Appendix300: Surgical video and patient metadata of 330 laparoscopic appendectomy cases from five institutions
(Technische Universität Dresden, 2026-04-29) Kolbinger, Fiona R; Kirchner, Max; Pfeiffer, Kevin; Bodenstedt, Sebastian; Jenke, Alexander C; Barthel, Julia; Carstens, Matthias; Dehlke, Karolin; Dietz, Sophia; Emmanouilidis, Sotirios; Fitze, Guido; Freitag, Martin; Holderried, Fabian; Jacobi, Thorsten; Kanjo, Weam; Leitermann, Linda; Mees, Sören Torge; Pistorius, Steffen; Prudlo, Conrad; Seiberth, Astrid; Schultz, Jurek; Thiel, Karolin; Ziehn, Daniel; Speidel, Stefanie; Kather, Jakob Nikolas; Distler, Marius; Saldanha, Oliver Lester
The limited availability of diverse and representative training data poses a critical barrier to the development of clinically relevant computational tools for intraoperative surgical decision support. Surgical procedures are not routinely recorded, and data annotation requires domain expertise, resulting in a scarcity of open-access surgical video datasets with high-quality annotations. Existing datasets are typically limited to single institutions and specific procedures, such as cholecystectomy, and rarely comprise patient-level metadata like demographic characteristics, disease history, or laboratory parameters. The Appendix300 dataset comprises 330 laparoscopic surgery recordings, including 325 full-length laparoscopic appendectomies and 5 control recordings from non-appendectomy procedures in pediatric and adult patients treated at five German centers. The dataset includes patient-level clinical metadata (demographics, medical history, clinical symptoms, preoperative laboratory parameters, and histopathological findings, as well as standardized expert annotations of the laparoscopic grade of appendicitis. This dataset enables novel validation tasks for computer vision in laparoscopic surgery and facilitates simulation of decentralized learning approaches, overall enhancing the breadth and translational relevance of AI-based surgical video analysis.
Open Access
Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark
(Technische Universität Dresden, 2024-02-15) van der Linden, Lize Mari; Wagner, Martin; Bodenstedt, Sebastian; Speidel, Stefanie
The data consists of endoscopic videos from general surgery operating rooms. The data was obtained during laparoscopic surgeries at the University Hospital of Heidelberg and its affiliate hospitals, forming a joint center of excellence for minimally invasive surgery. All surgeries were annotated framewise for surgical phases by surgical experts. Furthermore certain surgical actions, instrument usage and surgical skill levels were annotated. The surgeries recorded are laparoscopic gallbladder removals (cholecystectomy). The dataset consists out of at least 30 different recorded surgeries from three hospitals. For each surgery, the video captured by the endoscope is provided. To ensure anonymity, frames corresponding to extra-abdominal views are censored by entirely white (RGB 255 255 255) frames. The data will be released in three different sets: 2 training sets (the first set containing at least 12 videos and the second set containing at least 12 videos), which include framewise annotation of surgical phase, instrument usage, actions of surgeon and assistant as well as surgical skill. A testing set consisting of at least 6 videos will be provided.
Open Access
LASANA: Laparoscopic Skill Analysis and Assessment video dataset
(Technische Universität Dresden, 2025-12-26) Funke, Isabel; Bodenstedt, Sebastian; von Bechtolsheim, Felix; Oehme, Florian; Maruschke, Michael; Petzsch, Stefanie; Weitz, Jürgen; Distler, Marius; Mees, Sören Torge; Speidel, Stefanie
The LASANA video dataset comprises 1270 trimmed and synchronized stereo video recordings of four basic laparoscopic training tasks (peg transfer, circle cutting, balloon resection, and suture & knot). Per task, there are at least 311 recordings in the dataset. Each recording is annotated with a structured skill rating, aggregated across three independent raters, as well as binary labels indicating the presence or absence of task-specific errors (for example, dropping an object during the peg transfer task, or puncturing the inner balloon during the balloon resection task). The LASANA video dataset is intended to support the development and evaluation of methods for automatic video-based laparoscopic skill analysis. For benchmarking, a fixed datasplit into training, validation, and test videos is provided for each task.