Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos.

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

https://www.ncbi.nlm.nih.gov/pubmed/30968356

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;: