CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Creators: name: Nwoye, Chinedu Innocent
name: Alapatt, Deepak
name: Yu, Tong
name: Vardazaryan, Armine
name: Xia, Fangfang
name: Zhao, Zixuan
name: Xia, Tong
name: Jia, Fucang
name: Yang, Yuxuan
name: Wang, Hao
name: Yu, Derong
name: Zheng, Guoyan
name: Duan, Xiaotian
name: Getty, Neil
name: Sanchez-Matilla, Ricardo
name: Robu, Maria
name: Zhang, Li
name: Chen, Huabin
name: Wang, Jiacheng
name: Wang, Liansheng
name: Zhang, Bokai
name: Gerats, Beerend
name: Raviteja, Sista
name: Sathish, Rachana
name: Tao, Rong
name: Kondo, Satoshi
name: Pang, Winnie
name: Ren, Hongliang
name: Abbing, Julian Ronald
name: Sarhan, Mohammad Hasan
name: Bodenstedt, Sebastian
name: Bhasker, Nithya
name: Oliveira, Bruno
name: Torres, Helena R.
name: Ling, Li
name: Gaida, Finn
name: Czempiel, Tobias
name: Vilaça, João L.
name: Morais, Pedro
name: Fonseca, Jaime
name: Egging, Ruby Mae
name: Wijma, Inge Nicole
name: Qian, Chen
name: Bian, Guibin
name: Li, Zhen
name: Balasubramanian, Velmurugan
name: Sheet, Debdoot
name: Luengo, Imanol
name: Zhu, Yuanbo
name: Ding, Shuai
name: Aschenbrenner, Jakob-Anton
name: van der Kar, Nicolas Elini
name: Xu, Mengya
name: Islam, Mobarakol
name: Seenivasan, Lalithkumar
name: Jenke, Alexander
name: Stoyanov, Danail
name: Mutter, Didier
name: Mascagni, Pietro
name: Seeliger, Barbara
name: Gonzalez, Cristians
name: Padoy, Nicolas
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, 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 from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.
description: CholecTriplet2021 challenge report. Paper accepted at Elsevier journal of Medical Image Analysis. 22 pages, 8 figures, 11 tables. Challenge website: https://cholectriplet2021.grand-challenge.org