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SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge

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arxiv 2401.00496 v2 pith:OKMP4JNT submitted 2023-12-31 cs.CV cs.AIcs.LG

Dimitrios Psychogyios , Emanuele Colleoni , Beatrice Van Amsterdam , Chih-Yang Li , Shu-Yu Huang , Yuchong Li , Fucang Jia , Baosheng Zou
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Guotai Wang Yang Liu Maxence Boels Jiayu Huo Rachel Sparks Prokar Dasgupta Alejandro Granados Sebastien Ourselin Mengya Xu An Wang Yanan Wu Long Bai Hongliang Ren Atsushi Yamada Yuriko Harai Yuto Ishikawa Kazuyuki Hayashi Jente Simoens Pieter DeBacker Francesco Cisternino Gabriele Furnari Alex Mottrie Federica Ferraguti Satoshi Kondo Satoshi Kasai Kousuke Hirasawa Soohee Kim Seung Hyun Lee Kyu Eun Lee Hyoun-Joong Kong Kui Fu Chao Li Shan An Stefanie Krell Sebastian Bodenstedt Nicolas Ayobi Alejandra Perez Santiago Rodriguez Juanita Puentes Pablo Arbelaez Omid Mohareri Danail Stoyanov
This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords actionrecognitionsegmentationsurgicalchallengeapproachesdatasetinstrumentation
verification ladder T0 review T1 audit T2 compute T3 formal
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Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems. Nowadays, learning-based action recognition and segmentation approaches outperform classical methods, relying, however, on large, annotated datasets. Furthermore, action recognition and tool segmentation algorithms are often trained and make predictions in isolation from each other, without exploiting potential cross-task relationships. With the EndoVis 2022 SAR-RARP50 challenge, we release the first multimodal, publicly available, in-vivo, dataset for surgical action recognition and semantic instrumentation segmentation, containing 50 suturing video segments of Robotic Assisted Radical Prostatectomy (RARP). The aim of the challenge is twofold. First, to enable researchers to leverage the scale of the provided dataset and develop robust and highly accurate single-task action recognition and tool segmentation approaches in the surgical domain. Second, to further explore the potential of multitask-based learning approaches and determine their comparative advantage against their single-task counterparts. A total of 12 teams participated in the challenge, contributing 7 action recognition methods, 9 instrument segmentation techniques, and 4 multitask approaches that integrated both action recognition and instrument segmentation. The complete SAR-RARP50 dataset is available at: https://rdr.ucl.ac.uk/projects/SARRARP50_Segmentation_of_surgical_instrumentation_and_Action_Recognition_on_Robot-Assisted_Radical_Prostatectomy_Challenge/191091

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SurgNarrator: A Generative Retrieval Framework for Surgical Video Understanding

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A retrieval-based surgical video model that searches a surgery-specific concept vocabulary achieves state-of-the-art zero-shot results on most benchmarks at a fraction of generative latency.

  2. Stitch-Inferencer: Enhance Endoscopic Video Segmentation and Tracking via Panoramic Reconstruction

    cs.CV 2026-07 conditional novelty 6.0 of 10

    An inference-time framework builds an explicit panoramic memory from endoscopic video and uses it to improve off-the-shelf segmentation and tracking models without retraining.

  3. Attribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    ReferEndoscopy plus attribute-retrieval and frequency-aware fusion yields open-vocabulary compositional referring segmentation that outperforms natural-image RIS baselines on endoscopic data and generalizes to an unse...

  4. Exploring Pre-training Across Domains for Few-Shot Surgical Skill Assessment

    cs.CV 2025-09 conditional novelty 6.0 of 10

    For few-shot surgical skill assessment, pre-training on small but domain-relevant surgery videos outperforms large-scale, less aligned datasets, and mixing procedure-specific data only helps when the external source i...

  5. SurgPIS: Surgical-instrument-level Instances and Part-level Semantics for Weakly-supervised Part-aware Instance Segmentation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SurgPIS is a surgical part-aware instance segmentation model that predicts instrument instances and their parts together, and can learn from datasets labelled for only one of these tasks.

  6. SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking

    cs.CV 2025-11 conditional novelty 5.0 of 10

    SAM2S, a SAM2 variant trained on the new 61k-frame SA-SV surgical benchmark, improves average J&F to 80.42 at 68 FPS for interactive surgical-video object segmentation.

  7. EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A DINOv2-based multi-task framework with task-specific low-rank adapters and a spatial attention module reports state-of-the-art joint activity recognition and semantic segmentation on three endoscopic surgery datasets.

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