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<p>Indexing endoscopic surgical videos is vital in surgical data science, forming the basis for systematic retrospective analysis and clinical performance evaluation. Despite its significance, current video analytics rely on manual indexing, a time-consuming process. Advances in computer vision, particularly deep learning, offer automation potential, yet progress is limited by the lack of publicly available, densely annotated surgical datasets. To address this, we present TEMSET-24K, an open-source dataset comprising 24,306 trans-anal endoscopic microsurgery (TEMS) video microclips. Each clip is meticulously annotated by clinical experts using a novel hierarchical labeling taxonomy encompassing “phase, task, and action” triplets, capturing intricate surgical workflows. To validate this dataset, we benchmarked deep learning models, including transformer-based architectures. Our in silico evaluation demonstrates high accuracy (up to 0.99) and F1 scores (up to 0.99) for key phases like “Setup” and “Suturing.” The STALNet model, tested with ConvNeXt, ViT, and SWIN V2 encoders, consistently segmented well-represented phases. TEMSET-24K provides a critical benchmark, propelling state-of-the-art solutions in surgical data science.</p>

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TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月14日

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  • Muhammad Bilal,
  • Mahmood Alam,
  • Deepashree Bapu,
  • Stephan Korsgen,
  • Neeraj Lal,
  • Simon Bach,
  • Amir M. Hajiyavand,
  • Muhammed Ali,
  • Kamran Soomro,
  • Iqbal Qasim,
  • Paweł Capik,
  • Aslam Khan,
  • Zaheer Khan,
  • Hunaid Vohra,
  • Massimo Caputo,
  • Andrew D. Beggs,
  • Adnan Qayyum,
  • Junaid Qadir,
  • Shazad Q. Ashraf

摘要

Indexing endoscopic surgical videos is vital in surgical data science, forming the basis for systematic retrospective analysis and clinical performance evaluation. Despite its significance, current video analytics rely on manual indexing, a time-consuming process. Advances in computer vision, particularly deep learning, offer automation potential, yet progress is limited by the lack of publicly available, densely annotated surgical datasets. To address this, we present TEMSET-24K, an open-source dataset comprising 24,306 trans-anal endoscopic microsurgery (TEMS) video microclips. Each clip is meticulously annotated by clinical experts using a novel hierarchical labeling taxonomy encompassing “phase, task, and action” triplets, capturing intricate surgical workflows. To validate this dataset, we benchmarked deep learning models, including transformer-based architectures. Our in silico evaluation demonstrates high accuracy (up to 0.99) and F1 scores (up to 0.99) for key phases like “Setup” and “Suturing.” The STALNet model, tested with ConvNeXt, ViT, and SWIN V2 encoders, consistently segmented well-represented phases. TEMSET-24K provides a critical benchmark, propelling state-of-the-art solutions in surgical data science.