Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos.
Fiche publication
Date publication
avril 2019
Journal
International journal of computer assisted radiology and surgery
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MARESCAUX Jacques, Pr MUTTER Didier
Tous les auteurs :
Nwoye CI, Mutter D, Marescaux J, Padoy N
Lien Pubmed
Résumé
Real-time surgical tool tracking is a core component of the future intelligent operating room (OR), because it is highly instrumental to analyze and understand the surgical activities. Current methods for surgical tool tracking in videos need to be trained on data in which the spatial positions of the tools are manually annotated. Generating such training data is difficult and time-consuming. Instead, we propose to use solely binary presence annotations to train a tool tracker for laparoscopic videos.
Mots clés
ConvLSTM, Endoscopic videos, Spatiotemporal coherence, Surgical workflow analysis, Tool tracking, Weak supervision
Référence
Int J Comput Assist Radiol Surg. 2019 Apr 9;: