ProstaTD: A Large-scale Multi-source Dataset for Structured Surgical Triplet Detection

• URL: http://arxiv.org/abs/2506.01130v1
• Date: Sun, 01 Jun 2025 19:29:39 GMT
• Title: ProstaTD: A Large-scale Multi-source Dataset for Structured Surgical Triplet Detection
• Authors: Yiliang Chen, Zhixi Li, Cheng Xu, Alex Qinyang Liu, Xuemiao Xu, Jeremy Yuen-Chun Teoh, Shengfeng He, Jing Qin,
• Abstract summary: ProstaTD is a large-scale, multi-institutional dataset for surgical triplet detection.<n>It was developed from the technically demanding domain of robot-assisted prostatectomy.<n>The dataset comprises 60,529 video frames and 165,567 annotated triplet instances.
• Score: 34.96818119277855
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Surgical triplet detection has emerged as a pivotal task in surgical video analysis, with significant implications for performance assessment and the training of novice surgeons. However, existing datasets such as CholecT50 exhibit critical limitations: they lack precise spatial bounding box annotations, provide inconsistent and clinically ungrounded temporal labels, and rely on a single data source, which limits model generalizability.To address these shortcomings, we introduce ProstaTD, a large-scale, multi-institutional dataset for surgical triplet detection, developed from the technically demanding domain of robot-assisted prostatectomy. ProstaTD offers clinically defined temporal boundaries and high-precision bounding box annotations for each structured triplet action. The dataset comprises 60,529 video frames and 165,567 annotated triplet instances, collected from 21 surgeries performed across multiple institutions, reflecting a broad range of surgical practices and intraoperative conditions. The annotation process was conducted under rigorous medical supervision and involved more than 50 contributors, including practicing surgeons and medically trained annotators, through multiple iterative phases of labeling and verification. ProstaTD is the largest and most diverse surgical triplet dataset to date, providing a robust foundation for fair benchmarking, the development of reliable surgical AI systems, and scalable tools for procedural training.

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