Surgical site-specific ensemble model for surgical procedure segmentation

Kai Chen; Sreeram Kamabattula; Kiran Bhattacharyya

doi:10.1117/12.2691631

30 March 2024 Surgical site-specific ensemble model for surgical procedure segmentation

Kai Chen, Sreeram Kamabattula, Kiran Bhattacharyya

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129281L (2024) https://doi.org/10.1117/12.2691631
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Machine learning models that detect surgical activities in endoscopic videos are instrumental in scaling post-surgical video review tools that help surgeons improve their practice. However, it is unknown how well these models generalize across various surgical techniques practiced at different institutions. In this paper, we examined the possibility of using surgical site information for a more tailored, better-performing model on surgical procedure segmentation. Specifically, we developed an ensemble model consisting of site-specific models, meaning each individual model was trained on videos from a specific surgical site. We showed that the site-specific ensemble model consistently outperforms the state-of-the-art site-agnostic model. Furthermore, by examining the representation of video-frames in the latent space, we corroborated our findings with similarity metrics comparing videos within and across sites. Lastly, we proposed model deployment strategies to manage the introduction of videos from a new site or sites with insufficient data.

Citation Download Citation

Kai Chen, Sreeram Kamabattula, and Kiran Bhattacharyya "Surgical site-specific ensemble model for surgical procedure segmentation", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129281L (30 March 2024); https://doi.org/10.1117/12.2691631

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