TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910820366024704 |
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| author | Bilal, Muhammad Alam, Mahmood Bapu, Deepa Korsgen, Stephan Lal, Neeraj Bach, Simon Hajivanand, Amir M Ali, Muhammed Soomro, Kamran Qasim, Iqbal Capik, Paweł Khan, Aslam Khan, Zaheer Vohra, Hunaid Caputo, Massimo Beggs, Andrew Qayyum, Adnan Qadir, Junaid Ashraf, Shazad |
| author_facet | Bilal, Muhammad Alam, Mahmood Bapu, Deepa Korsgen, Stephan Lal, Neeraj Bach, Simon Hajivanand, Amir M Ali, Muhammed Soomro, Kamran Qasim, Iqbal Capik, Paweł Khan, Aslam Khan, Zaheer Vohra, Hunaid Caputo, Massimo Beggs, Andrew Qayyum, Adnan Qadir, Junaid Ashraf, Shazad |
| contents | 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 micro-clips. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06708 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation Bilal, Muhammad Alam, Mahmood Bapu, Deepa Korsgen, Stephan Lal, Neeraj Bach, Simon Hajivanand, Amir M Ali, Muhammed Soomro, Kamran Qasim, Iqbal Capik, Paweł Khan, Aslam Khan, Zaheer Vohra, Hunaid Caputo, Massimo Beggs, Andrew Qayyum, Adnan Qadir, Junaid Ashraf, Shazad Computer Vision and Pattern Recognition 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 micro-clips. 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. |
| title | TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.06708 |