TY - CHAP
T1 - Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning
AU - Huber, Martin
AU - Ourselin, Sébastien
AU - Bergeles, Christos
AU - Vercauteren, Tom
N1 - Funding Information:
Acknowledgements. This work was supported by core and project funding from the Wellcome/EPSRC [WT203148/Z/16/Z; NS/A000049/1; WT101957; NS/A000027/1]. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016985 (FAROS project). TV is supported by a Medtronic/RAEng Research Chair [RCSRF1819\7\34]. SO and TV are co-founders and shareholders of Hypervision Surgical. TV is co-founder and shareholder of Hypervision Surgical. TV holds shares from Mauna Kea Technologies.
Funding Information:
This work was supported by core and project funding from the Wellcome/EPSRC [WT203148/Z/16/Z; NS/A000049/1; WT101957; NS/A000027/1]. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016985 (FAROS project). TV is supported by a Medtronic/RAEng Research Chair [RCSRF1819\7\34]. SO and TV are co-founders and shareholders of Hypervision Surgical. TV is co-founder and shareholder of Hypervision Surgical. TV holds shares from Mauna Kea Technologies.
Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023/10
Y1 - 2023/10
N2 - In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions. A novel method is introduced that learns to augment a surgeon’s behavior in image space through object motion invariant image registration via homographies. Contrary to existing approaches, no geometric assumptions are made and no depth information is necessary, enabling immediate translation to a robotic setup. Deviating from the dominant approach in the literature which consist of following a surgical tool, we do not handcraft the objective and no priors are imposed on the surgical scene, allowing the method to discover unbiased policies. In this new research field, significant improvements are demonstrated over two baselines on the Cholec80 and HeiChole datasets, showcasing an improvement of 47 % over camera motion continuation. The method is further shown to indeed predict camera motion correctly on the public motion classification labels of the AutoLaparo dataset. All code is made accessible on GitHub (https://github.com/RViMLab/homography_imitation_learning ).
AB - In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions. A novel method is introduced that learns to augment a surgeon’s behavior in image space through object motion invariant image registration via homographies. Contrary to existing approaches, no geometric assumptions are made and no depth information is necessary, enabling immediate translation to a robotic setup. Deviating from the dominant approach in the literature which consist of following a surgical tool, we do not handcraft the objective and no priors are imposed on the surgical scene, allowing the method to discover unbiased policies. In this new research field, significant improvements are demonstrated over two baselines on the Cholec80 and HeiChole datasets, showcasing an improvement of 47 % over camera motion continuation. The method is further shown to indeed predict camera motion correctly on the public motion classification labels of the AutoLaparo dataset. All code is made accessible on GitHub (https://github.com/RViMLab/homography_imitation_learning ).
KW - Computer vision
KW - Imitation learning
KW - Robotic surgery
UR - http://www.scopus.com/inward/record.url?scp=85174724734&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43996-4_21
DO - 10.1007/978-3-031-43996-4_21
M3 - Conference paper
AN - SCOPUS:85174724734
SN - 9783031439957
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 217
EP - 226
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
A2 - Greenspan, Hayit
A2 - Greenspan, Hayit
A2 - Madabhushi, Anant
A2 - Mousavi, Parvin
A2 - Salcudean, Septimiu
A2 - Duncan, James
A2 - Syeda-Mahmood, Tanveer
A2 - Taylor, Russell
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Y2 - 8 October 2023 through 12 October 2023
ER -