Live laparoscopic video retrieval with compressed uncertainty.

Fiche publication

Date publication

juin 2023

Journal

Medical image analysis

Auteurs

Membres identifiés du Cancéropôle Est :
Pr MARESCAUX Jacques , Pr MUTTER Didier

Tous les auteurs :
Yu T, Mascagni P, Verde J, Marescaux J, Mutter D, Padoy N

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/37356320

Résumé

Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care. However the primitive and most common approach to retrieval, involving text in the form of keywords, is severely limited when dealing with complex media formats. Content-based retrieval offers a way to overcome this limitation, by using rich media as the query itself. Surgical video-to-video retrieval in particular is a new and largely unexplored research problem with high clinical value, especially in the real-time case: using real-time video hashing, search can be achieved directly inside of the operating room. Indeed, the process of hashing converts large data entries into compact binary arrays or hashes, enabling large-scale search operations at a very fast rate. However, due to fluctuations over the course of a video, not all bits in a given hash are equally reliable. In this work, we propose a method capable of mitigating this uncertainty while maintaining a light computational footprint. We present superior retrieval results (3%-4% top 10 mean average precision) on a multi-task evaluation protocol for surgery, using cholecystectomy phases, bypass phases, and coming from an entirely new dataset introduced here, surgical events across six different surgery types. Success on this multi-task benchmark shows the generalizability of our approach for surgical video retrieval.