Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge

no code implementations • 11 May 2023 • Aneeq Zia, Kiran Bhattacharyya, Xi Liu, Max Berniker, Ziheng Wang, Rogerio Nespolo, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Bo Liu, David Austin, Yiheng Wang, Michal Futrega, Jean-Francois Puget, Zhenqiang Li, Yoichi Sato, Ryo Fujii, Ryo Hachiuma, Mana Masuda, Hideo Saito, An Wang, Mengya Xu, Mobarakol Islam, Long Bai, Winnie Pang, Hongliang Ren, Chinedu Nwoye, Luca Sestini, Nicolas Padoy, Maximilian Nielsen, Samuel Schüttler, Thilo Sentker, Hümeyra Husseini, Ivo Baltruschat, Rüdiger Schmitz, René Werner, Aleksandr Matsun, Mugariya Farooq, Numan Saaed, Jose Renato Restom Viera, Mohammad Yaqub, Neil Getty, Fangfang Xia, Zixuan Zhao, Xiaotian Duan, Xing Yao, Ange Lou, Hao Yang, Jintong Han, Jack Noble, Jie Ying Wu, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Herag Arabian, Ning Ding, Knut Moeller, Weiliang Chen, Quan He, Muhammad Bilal, Taofeek Akinosho, Adnan Qayyum, Massimo Caputo, Hunaid Vohra, Michael Loizou, Anuoluwapo Ajayi, Ilhem Berrou, Faatihah Niyi-Odumosu, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel, Anthony Jarc

Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.

Automatic Detection of Out-of-body Frames in Surgical Videos for Privacy Protection Using Self-supervised Learning and Minimal Labels

no code implementations • 31 Mar 2023 • Ziheng Wang, Conor Perreault, Xi Liu, Anthony Jarc

Endoscopic video recordings are widely used in minimally invasive robot-assisted surgery, but when the endoscope is outside the patient's body, it can capture irrelevant segments that may contain sensitive information.

Self-Supervised Learning

Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge

no code implementations • 8 Dec 2022 • Aneeq Zia, Kiran Bhattacharyya, Xi Liu, Ziheng Wang, Max Berniker, Satoshi Kondo, Emanuele Colleoni, Dimitris Psychogyios, Yueming Jin, Jinfan Zhou, Evangelos Mazomenos, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel, Anthony Jarc

Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science.

Skills Assessment

Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge

no code implementations • 26 Feb 2021 • Aneeq Zia, Kiran Bhattacharyya, Xi Liu, Ziheng Wang, Satoshi Kondo, Emanuele Colleoni, Beatrice van Amsterdam, Razeen Hussain, Raabid Hussain, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel, Anthony Jarc

Surgical data science is revolutionizing minimally invasive surgery by enabling context-aware applications.

Domain Adaptation

Novel evaluation of surgical activity recognition models using task-based efficiency metrics

no code implementations • 3 Jul 2019 • Aneeq Zia, Liheng Guo, Linlin Zhou, Irfan Essa, Anthony Jarc

Conclusions: We demonstrate that metrics-based evaluation of surgical activity recognition models is a viable approach to determine when models can be used to quantify surgical efficiencies.

Activity Recognition

Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning

no code implementations • 1 Jun 2018 • Aneeq Zia, Andrew Hung, Irfan Essa, Anthony Jarc

Adverse surgical outcomes are costly to patients and hospitals.

Activity Recognition