Evaluating unsupervised optical flow for keypoint tracking in laparoscopic videos

Bruno Silva; Sandro Queirós; Marcos Fernández-Rodríguez; Bruno Oliveira; Helena R. Torres; Pedro Morais; Lukas R. Buschle; Jorge Correia-Pinto; Estevão Lima; João L. Vilaça

doi:10.1117/12.3006405

29 March 2024 Evaluating unsupervised optical flow for keypoint tracking in laparoscopic videos

Bruno Silva, Sandro Queirós, Marcos Fernández-Rodríguez, Bruno Oliveira, Helena R. Torres, Pedro Morais, Lukas R. Buschle, Jorge Correia-Pinto, Estevão Lima, João L. Vilaça

Author Affiliations +

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129281V (2024) https://doi.org/10.1117/12.3006405
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Inspired by the ”What Matters in Unsupervised Optical Flow” study, the goal of this work is to evaluate the performance of the ARFlow architecture for unsupervised optical flow in the context of tracking keypoints in laparoscopic videos. This assessment could provide insight into the applicability of ARFlow and similar architectures for this particular application, as well as their strengths and limitations. To do so, we use the SurgT challenge’s dataset and metrics to evaluate the tracker’s accuracy and robustness and its relationship with distinct network components. Our results corroborate some of the findings reported by Jonschkowski et al. However, certain components demonstrate a distinct behavior, possibly indicating underlying issues, namely intrinsic to the application, that impact overall performance and which may have to be addressed in the context of soft-tissue trackers. These results point to potential bottlenecks and areas where future work may target on.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Bruno Silva, Sandro Queirós, Marcos Fernández-Rodríguez, Bruno Oliveira, Helena R. Torres, Pedro Morais, Lukas R. Buschle, Jorge Correia-Pinto, Estevão Lima, and João L. Vilaça "Evaluating unsupervised optical flow for keypoint tracking in laparoscopic videos", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129281V (29 March 2024); https://doi.org/10.1117/12.3006405

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