Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures.

Date publication

mai 2021

Journal

International journal of computer assisted radiology and surgery

Auteurs

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

Tous les auteurs :
Ramesh S, Dall'Alba D, Gonzalez C, Yu T, Mascagni P, Mutter D, Marescaux J, Fiorini P, Padoy N

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/34013464

Résumé

Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities in robot-assisted surgeries. Prior works have focused on recognizing either coarse activities, such as phases, or fine-grained activities, such as gestures. This work aims at jointly recognizing two complementary levels of granularity directly from videos, namely phases and steps.

Mots clés

Surgical workflow analysis, deep learning, endoscopic videos, laparoscopic gastric bypass, multi-task learning, temporal modeling

Référence

Int J Comput Assist Radiol Surg. 2021 May 19;: