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

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.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-08-14
DOI
https://doi.org/10.1038/s41597-025-05646-w
Citations
1
Primary Topic
Surgical Simulation and Training
Type
article
Field-Weighted Citation Impact
2.01
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Deepashree Bapu, Shazad Q. Ashraf, Massimo Caputo, Amir M. Hajiyavand et al.
1 citations
Scientific Data
Surgical Simulation and Training
2.01
article

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

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

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.

Scientific DataVol. 12(1)
Birmingham City University (GB), Information Technology University (PK), University of Bradford (GB), University of Hertfordshire (GB), University of Auckland (NZ), University of the West of England (GB), University of Bristol (GB), NIHR Surgical Reconstruction and Microbiology Research Centre (GB), Qatar University (QA), University of Birmingham (GB)
Openalex Percentile: Top 13%
Surgical Simulation and Training
2.01
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.