Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge

• URL: http://arxiv.org/abs/2507.16559v1
• Date: Tue, 22 Jul 2025 13:10:42 GMT
• Title: Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge
• Authors: Tobias Rueckert, David Rauber, Raphaela Maerkl, Leonard Klausmann, Suemeyye R. Yildiran, Max Gutbrod, Danilo Weber Nunes, Alvaro Fernandez Moreno, Imanol Luengo, Danail Stoyanov, Nicolas Toussaint, Enki Cho, Hyeon Bae Kim, Oh Sung Choo, Ka Young Kim, Seong Tae Kim, Gonçalo Arantes, Kehan Song, Jianjun Zhu, Junchen Xiong, Tingyi Lin, Shunsuke Kikuchi, Hiroki Matsuzaki, Atsushi Kouno, João Renato Ribeiro Manesco, João Paulo Papa, Tae-Min Choi, Tae Kyeong Jeong, Juyoun Park, Oluwatosin Alabi, Meng Wei, Tom Vercauteren, Runzhi Wu, Mengya Xu, An Wang, Long Bai, Hongliang Ren, Amine Yamlahi, Jakob Hennighausen, Lena Maier-Hein, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Shu Yang, Yihui Wang, Hao Chen, Santiago Rodríguez, Nicolás Aparicio, Leonardo Manrique, Juan Camilo Lyons, Olivia Hosie, Nicolás Ayobi, Pablo Arbeláez, Yiping Li, Yasmina Al Khalil, Sahar Nasirihaghighi, Stefanie Speidel, Daniel Rueckert, Hubertus Feussner, Dirk Wilhelm, Christoph Palm,
• Abstract summary: We introduce a novel dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three medical institutions.<n>Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data.<n>We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges.
• Score: 27.48982385201173
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding.

Related papers

• Surgeons vs. Computer Vision: A comparative analysis on surgical phase recognition capabilities [65.66373425605278]
  Automated Surgical Phase Recognition (SPR) uses Artificial Intelligence (AI) to segment the surgical workflow into its key events.<n>Previous research has focused on short and linear surgical procedures and has not explored if temporal context influences experts' ability to better classify surgical phases.<n>This research addresses these gaps, focusing on Robot-Assisted Partial Nephrectomy (RAPN) as a highly non-linear procedure.
  arXiv  Detail & Related papers  (2025-04-26T15:37:22Z)
• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• Pixel-Wise Recognition for Holistic Surgical Scene Understanding [33.40319680006502]
  This paper presents the Holistic and Multi-Granular Surgical Scene Understanding of Prostatectomies dataset.<n>Our benchmark models surgical scene understanding as a hierarchy of complementary tasks with varying levels of granularity.<n>To exploit our proposed benchmark, we introduce the Transformers for Actions, Phases, Steps, and Instrument (TAPIS) model.
  arXiv  Detail & Related papers  (2024-01-20T09:09:52Z)
• SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge [72.97934765570069]
  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 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. 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.
  arXiv  Detail & Related papers  (2023-12-31T13:32:18Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection [41.66666272822756]
  This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool) as the key actors, and the modeling of each tool-activity in the form of instrument, verb, target> triplet.
  arXiv  Detail & Related papers  (2023-02-13T11:53:14Z)
• Towards Holistic Surgical Scene Understanding [1.004785607987398]
  We present a new experimental framework towards holistic surgical scene understanding. First, we introduce the Phase, Step, Instrument, and Atomic Visual Action recognition (PSI-AVA) dataset. Second, we present Transformers for Action, Phase, Instrument, and steps Recognition (TAPIR) as a strong baseline for surgical scene understanding.
  arXiv  Detail & Related papers  (2022-12-08T22:15:27Z)
• CholecTriplet2021: A benchmark challenge for surgical action triplet recognition [66.51610049869393]
  This paper presents CholecTriplet 2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. We present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods and 19 new deep learning algorithms are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%.
  arXiv  Detail & Related papers  (2022-04-10T18:51:55Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.