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