no code implementations • 22 Feb 2024 • Jamshid Hassanpour, Vinkle Srivastav, Didier Mutter, Nicolas Padoy
In this work, we investigate the application of contrastive learning to the domain of medical image analysis.
no code implementations • 19 Dec 2023 • Idris Hamoud, Muhammad Abdullah Jamal, Vinkle Srivastav, Didier Mutter, Nicolas Padoy, Omid Mohareri
Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR).
1 code implementation • 19 Dec 2023 • Aditya Murali, Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Guido Costamagna, Didier Mutter, Jacques Marescaux, Bernard Dallemagne, Nicolas Padoy
This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS).
1 code implementation • 18 Dec 2023 • Joel L. Lavanchy, Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez, Paolo Fiorini, Beat Muller-Stich, Philipp C. Nett, Jacques Marescaux, Didier Mutter, Nicolas Padoy
The use of multi-centric training data, experiments 6) and 7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments 1) and 2)).
1 code implementation • 11 Dec 2023 • Aditya Murali, Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Didier Mutter, Nicolas Padoy
Recently, spatiotemporal graphs have emerged as a concise and elegant manner of representing video clips in an object-centric fashion, and have shown to be useful for downstream tasks such as action recognition.
no code implementations • 19 Oct 2023 • William Ndzimbong, Cyril Fourniol, Loic Themyr, Nicolas Thome, Yvonne Keeza, Beniot Sauer, Pierre-Thierry Piechaud, Arnaud Mejean, Jacques Marescaux, Daniel George, Didier Mutter, Alexandre Hostettler, Toby Collins
To validate the dataset's utility, 5 competitive Deep Learning models for automatic kidney segmentation were benchmarked, yielding average DICE scores from 83. 2% to 89. 1% for CT, and 61. 9% to 79. 4% for US images.
1 code implementation • 18 Jul 2023 • Saurav Sharma, Chinedu Innocent Nwoye, Didier Mutter, Nicolas Padoy
We analyze how the amount of instrument spatial annotations affects triplet detection and observe that accurate instrument localization does not guarantee better triplet detection due to the risk of erroneous associations with the verbs and targets.
no code implementations • 21 Feb 2023 • Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez, Tong Yu, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Paolo Fiorini, Nicolas Padoy
In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos.
2 code implementations • 13 Feb 2023 • Chinedu Innocent Nwoye, Tong Yu, Saurav Sharma, Aditya Murali, Deepak Alapatt, Armine Vardazaryan, Kun Yuan, Jonas Hajek, Wolfgang Reiter, Amine Yamlahi, Finn-Henri Smidt, Xiaoyang Zou, Guoyan Zheng, Bruno Oliveira, Helena R. Torres, Satoshi Kondo, Satoshi Kasai, Felix Holm, Ege Özsoy, Shuangchun Gui, Han Li, Sista Raviteja, Rachana Sathish, Pranav Poudel, Binod Bhattarai, Ziheng Wang, Guo Rui, Melanie Schellenberg, João L. Vilaça, Tobias Czempiel, Zhenkun Wang, Debdoot Sheet, Shrawan Kumar Thapa, Max Berniker, Patrick Godau, Pedro Morais, Sudarshan Regmi, Thuy Nuong Tran, Jaime Fonseca, Jan-Hinrich Nölke, Estevão Lima, Eduard Vazquez, Lena Maier-Hein, Nassir Navab, Pietro Mascagni, Barbara Seeliger, Cristians Gonzalez, Didier Mutter, Nicolas Padoy
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on Action Triplet Detection on CholecT50 (Challenge)
no code implementations • 17 Jan 2023 • Joël L. Lavanchy, Armine Vardazaryan, Pietro Mascagni, AI4SafeChole Consortium, Didier Mutter, Nicolas Padoy
Results: The internal dataset consisting of 356, 267 images from 48 videos and the two multicentric test datasets consisting of 54, 385 and 58, 349 images from 10 and 20 videos, respectively, were annotated.
no code implementations • 13 Dec 2022 • Pietro Mascagni, Deepak Alapatt, Alfonso Lapergola, Armine Vardazaryan, Jean-Paul Mazellier, Bernard Dallemagne, Didier Mutter, Nicolas Padoy
Artificial intelligence is set to be deployed in operating rooms to improve surgical care.
1 code implementation • 8 Dec 2022 • Aditya Murali, Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Didier Mutter, Nicolas Padoy
Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure.
1 code implementation • 30 Nov 2022 • Saurav Sharma, Chinedu Innocent Nwoye, Didier Mutter, Nicolas Padoy
Focusing more on the verbs, our RiT explores the connectedness of current and past frames to learn temporal attention-based features for enhanced triplet recognition.
Ranked #1 on Action Triplet Recognition on CholecT45 (cross-val)
6 code implementations • 10 Apr 2022 • Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu, Armine Vardazaryan, Fangfang Xia, Zixuan Zhao, Tong Xia, Fucang Jia, Yuxuan Yang, Hao Wang, Derong Yu, Guoyan Zheng, Xiaotian Duan, Neil Getty, Ricardo Sanchez-Matilla, Maria Robu, Li Zhang, Huabin Chen, Jiacheng Wang, Liansheng Wang, Bokai Zhang, Beerend Gerats, Sista Raviteja, Rachana Sathish, Rong Tao, Satoshi Kondo, Winnie Pang, Hongliang Ren, Julian Ronald Abbing, Mohammad Hasan Sarhan, Sebastian Bodenstedt, Nithya Bhasker, Bruno Oliveira, Helena R. Torres, Li Ling, Finn Gaida, Tobias Czempiel, João L. Vilaça, Pedro Morais, Jaime Fonseca, Ruby Mae Egging, Inge Nicole Wijma, Chen Qian, GuiBin Bian, Zhen Li, Velmurugan Balasubramanian, Debdoot Sheet, Imanol Luengo, Yuanbo Zhu, Shuai Ding, Jakob-Anton Aschenbrenner, Nicolas Elini van der Kar, Mengya Xu, Mobarakol Islam, Lalithkumar Seenivasan, Alexander Jenke, Danail Stoyanov, Didier Mutter, Pietro Mascagni, Barbara Seeliger, Cristians Gonzalez, Nicolas Padoy
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.
Ranked #1 on Action Triplet Recognition on CholecT50 (Challenge) (using extra training data)
no code implementations • 8 Mar 2022 • Tong Yu, Pietro Mascagni, Juan Verde, Jacques Marescaux, Didier Mutter, Nicolas Padoy
Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care.
no code implementations • 27 Dec 2021 • Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Didier Mutter, Jacques Marescaux, Guido Costamagna, Bernard Dallemagne, Nicolas Padoy
A major obstacle to building models for effective semantic segmentation, and particularly video semantic segmentation, is a lack of large and well annotated datasets.
Ranked #2 on Semantic Segmentation on Endoscapes
8 code implementations • 7 Sep 2021 • Chinedu Innocent Nwoye, Tong Yu, Cristians Gonzalez, Barbara Seeliger, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Nicolas Padoy
To achieve this task, we introduce our new model, the Rendezvous (RDV), which recognizes triplets directly from surgical videos by leveraging attention at two different levels.
Ranked #1 on Action Triplet Recognition on CholecT50
no code implementations • 6 Apr 2021 • Pietro Mascagni, Maria Rita Rodriguez-Luna, Takeshi Urade, Emanuele Felli, Patrick Pessaux, Didier Mutter, Jacques Marescaux, Guido Costamagna, Bernard Dallemagne, Nicolas Padoy
The primary endpoint was to compare the rate of CVS achievement between LCs performed in the year before and the year after the 5-second rule.
no code implementations • 24 Feb 2021 • Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez, Tong Yu, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Paolo Fiorini, Nicolas Padoy
Conclusion: In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on the Bypass40 gastric bypass dataset with multi-level annotations.
4 code implementations • 10 Jul 2020 • Chinedu Innocent Nwoye, Cristians Gonzalez, Tong Yu, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room.
Ranked #1 on Action Triplet Recognition on CholecT40
1 code implementation • 4 Dec 2018 • Chinedu Innocent Nwoye, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Results: We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over 5. 0%, 13. 9%, and 12. 6%, respectively.
Ranked #2 on Surgical tool detection on Cholec80
1 code implementation • 30 Nov 2018 • Tong Yu, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior.
no code implementations • 28 Nov 2018 • Siddharth Kannan, Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy
This work presents a novel approach for the early recognition of the type of a laparoscopic surgery from its video.
1 code implementation • 14 Jun 2018 • Armine Vardazaryan, Didier Mutter, Jacques Marescaux, Nicolas Padoy
We propose a deep architecture, trained solely on image level annotations, that can be used for both tool presence detection and localization in surgical videos.
Ranked #4 on Surgical tool detection on Cholec80
no code implementations • 22 May 2018 • Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy
In this work, we propose a new self-supervised pre-training approach based on the prediction of remaining surgery duration (RSD) from laparoscopic videos.
1 code implementation • 9 Feb 2018 • Andru Putra Twinanda, Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy
In this paper, we propose a deep learning pipeline, referred to as RSDNet, which automatically estimates the remaining surgery duration (RSD) intraoperatively by using only visual information from laparoscopic videos.
no code implementations • 27 Oct 2016 • Andru P. Twinanda, Didier Mutter, Jacques Marescaux, Michel de Mathelin, Nicolas Padoy
The tool presence detection challenge at M2CAI 2016 consists of identifying the presence/absence of seven surgical tools in the images of cholecystectomy videos.
no code implementations • 27 Oct 2016 • Andru P. Twinanda, Didier Mutter, Jacques Marescaux, Michel de Mathelin, Nicolas Padoy
On top of these architectures we propose to use two different approaches to enforce the temporal constraints of the surgical workflow: (1) HMM-based and (2) LSTM-based pipelines.
9 code implementations • 9 Feb 2016 • Andru P. Twinanda, Sherif Shehata, Didier Mutter, Jacques Marescaux, Michel de Mathelin, Nicolas Padoy
In the literature, two types of features are typically used to perform this task: visual features and tool usage signals.
Ranked #5 on Surgical tool detection on Cholec80
Offline surgical phase recognition Online surgical phase recognition +2