Latent Graph Representations for Critical View of Safety Assessment DOI Creative Commons
Aditya Murali, Deepak Alapatt, Pietro Mascagni

и другие.

arXiv (Cornell University), Год журнала: 2022, Номер unknown

Опубликована: Янв. 1, 2022

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization key anatomical structures, reasoning about their geometric relationships to one another, determining quality exposure. Prior works have approached this task by including semantic segmentation as an intermediate step, using predicted masks then predict CVS. While these methods are effective, they rely on extremely expensive ground-truth annotations tend fail when is incorrect, limiting generalization. In work, we propose a method for CVS prediction wherein first represent surgical image disentangled latent scene graph, process representation graph neural network. Our representations explicitly encode information - object location, class information, relations improve anatomy-driven reasoning, well visual features retain differentiability thereby provide robustness errors. Finally, address annotation cost, train our only bounding box annotations, incorporating auxiliary reconstruction objective learn fine-grained boundaries. We show that not outperforms several baseline trained with but also scales effectively masks, maintaining state-of-the-art performance.

Язык: Английский

Use of artificial intelligence in total mesorectal excision in rectal cancer surgery: State of the art and perspectives DOI Open Access
Vinicio Mosca, Giacomo Fuschillo, Guido Sciaudone

и другие.

Artificial Intelligence in Gastroenterology, Год журнала: 2023, Номер 4(3), С. 64 - 71

Опубликована: Дек. 7, 2023

BACKGROUND Colorectal cancer is a major public health problem, with 1.9 million new cases and 953000 deaths worldwide in 2020. Total mesorectal excision (TME) the standard of care for treatment rectal crucial to prevent local recurrence, but it technically challenging surgery. The use artificial intelligence (AI) could help improve performance safety TME AIM To review literature on AI machine learning surgery potential future developments. METHODS Online scientific databases were searched articles between 2020 2023. RESULTS search yielded 876 results, only 13 studies selected review. specifically rapidly evolving field. There are number different algorithms that have been developed TME, including instrument detection, anatomical structure identification, image-guided navigation systems. CONCLUSION has revolutionize by providing real-time surgical guidance, preventing complications, improving training. However, further research needed fully understand benefits risks

Язык: Английский

Процитировано

0

Latent Graph Representations for Critical View of Safety Assessment DOI Creative Commons
Aditya Murali, Deepak Alapatt, Pietro Mascagni

и другие.

arXiv (Cornell University), Год журнала: 2022, Номер unknown

Опубликована: Янв. 1, 2022

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization key anatomical structures, reasoning about their geometric relationships to one another, determining quality exposure. Prior works have approached this task by including semantic segmentation as an intermediate step, using predicted masks then predict CVS. While these methods are effective, they rely on extremely expensive ground-truth annotations tend fail when is incorrect, limiting generalization. In work, we propose a method for CVS prediction wherein first represent surgical image disentangled latent scene graph, process representation graph neural network. Our representations explicitly encode information - object location, class information, relations improve anatomy-driven reasoning, well visual features retain differentiability thereby provide robustness errors. Finally, address annotation cost, train our only bounding box annotations, incorporating auxiliary reconstruction objective learn fine-grained boundaries. We show that not outperforms several baseline trained with but also scales effectively masks, maintaining state-of-the-art performance.

Язык: Английский

Процитировано

0