Browsing by Author "Wagner, Martin"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • ItemOpen 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.
  • ItemOpen Access
    The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes
    (Technische Universität Dresden, 2025-12-10) Docea, Reuben; Younis, Rayan; Long, Yonghao; Fleury, Maxime; Xu, Jinjing; Li, Chenyang; Schulze, André; Wierick, Ann; Bender, Johannes; Pfeiffer, Micha; Dou, Qi; Wagner, Martin; Speidel, Stefanie
    The D4D Dataset provides paired endoscopic video and high-quality structured-light geometry for evaluating 3D reconstruction of deforming abdominal soft tissue in realistic surgical conditions. Data were acquired from six porcine cadaver sessions using a da Vinci Xi stereo endoscope and a Zivid structured-light camera, registered via optical tracking and manually curated iterative alignment methods. Three sequence types - whole deformations, incremental deformations, and moved-camera clips - probe algorithm robustness to non-rigid motion, deformation magnitude, and out-of-view updates. Each clip provides rectified stereo images, per-frame instrument masks, stereo depth, start/end structured-light point clouds, curated camera poses and camera intrinsics. In postprocessing, ICP and semi-automatic registration techniques are used to register data, and instrument masks are created. The dataset enables quantitative geometric evaluation in both visible and occluded regions, alongside photometric view-synthesis baselines. Comprising over 300,000 frames and 369 point clouds across 98 curated recordings, this resource can serve as a comprehensive benchmark for developing and evaluating non-rigid SLAM, 4D reconstruction, and depth estimation methods.