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

Bilal, Muhammad and Alam, Mahmood and Bapu, Deepashree and Korsgen, Stephan and Lal, Neeraj and Bach, Simon and Hajiyavand, Amir M. and Ali, Muhammed and Soomro, Kamran and Qasim, Iqbal and Capik, Paweł and Khan, Aslam and Khan, Zaheer and Vohra, Hunaid and Caputo, Massimo and Beggs, Andrew D. and Qayyum, Adnan and Qadir, Junaid and Ashraf, Shazad Q. (2025) TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation. Scientific Data, 12 (1). ISSN 2052-4463

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Abstract

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.

Item Type:	Article
Identification Number:	10.1038/s41597-025-05646-w
Dates:	Date Event 18 July 2025 Accepted 14 August 2025 Published Online
Subjects:	CAH17 - business and management > CAH17-01 - business and management > CAH17-01-01 - business and management (non-specific)
Divisions:	Business School > Management, Business and Marketing
Depositing User:	Gemma Tonks
Date Deposited:	04 Sep 2026 09:52
Last Modified:	04 Sep 2026 09:52
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17210

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