eprintid: 10193373 rev_number: 15 eprint_status: archive userid: 699 dir: disk0/10/19/33/73 datestamp: 2024-08-20 13:08:14 lastmod: 2024-08-20 13:08:14 status_changed: 2024-08-20 13:08:14 type: thesis metadata_visibility: show sword_depositor: 699 creators_name: van Amsterdam, Beatrice title: Surgical Gesture Recognition in Robot-Assisted Laparoscopic Surgery ispublished: unpub divisions: B04 divisions: C05 divisions: F42 divisions: UCL note: Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. abstract: Automated surgical gesture recognition is a crucial step in developing systems for surgical data science, analytics, objective skill evaluation, and surgical automation. However, it is a challenging task due to the high variability of surgical gestures caused by various factors such as the surgeon's style and the patient's anatomy. This variability leads to differences in the duration, kinematics, and order of actions across different demonstrations, making the analysis and understanding of surgical instrument kinematics and video prone to errors. Another challenge is the lack of large and diverse datasets with annotated demonstrations, which are essential for training robust recognition systems based on machine learning and deep learning. Manual labeling of surgical gestures is costly, time-consuming, and prone to errors and inconsistencies. The goal of this thesis is to propose new strategies for integrating complementary information, such as different tasks or input modalities, to improve recognition performance and reduce the reliance on large training datasets. The thesis first presents a multi-task network for surgeme recognition and progress prediction, which achieves improved performance without the need for additional manual labeling and training. The thesis also focuses on data fusion for action recognition. It explores methods to mitigate prediction errors caused by individual sensor noise and limitations by dynamically weighting each modality over time. Additionally, it investigates techniques for enhancing multimodal representation learning by separating hidden features into modality-shared and private components. In both cases, the experiments demonstrate performance improvements over unimodal baselines and common multimodal fusion strategies. The evaluation is conducted on established benchmark datasets and a newly developed dataset of real surgical interventions. By addressing the challenges of surgical gesture recognition through the integration of complementary information and the development of new datasets, this thesis contributes to advancing the field and enhancing the applicability of recognition systems in real surgical settings. date: 2024-06-28 date_type: published oa_status: green full_text_type: other thesis_class: doctoral_open thesis_award: Ph.D language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2284676 lyricists_name: Van Amsterdam, Beatrice lyricists_id: BVANA59 actors_name: Van Amsterdam, Beatrice actors_id: BVANA59 actors_role: owner full_text_status: public pages: 152 institution: UCL (University College London) department: Medical Physics and Biomedical Engineering thesis_type: Doctoral citation: van Amsterdam, Beatrice; (2024) Surgical Gesture Recognition in Robot-Assisted Laparoscopic Surgery. Doctoral thesis (Ph.D), UCL (University College London). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10193373/2/vanAmsterdam_PhD.pdf