EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos.
Fiche publication
Date publication
janvier 2017
Journal
IEEE transactions on medical imaging
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MARESCAUX Jacques, Pr DE MATHELIN Michel, Pr MUTTER Didier
Tous les auteurs :
Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N
Lien Pubmed
Résumé
Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.
Référence
IEEE Trans Med Imaging. 2017 Jan;36(1):86-97