EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

TL;DR

EndoNet introduces a multi-task CNN architecture that automatically learns visual features for surgical phase recognition and tool detection in laparoscopic videos, achieving state-of-the-art results without manual annotations or additional equipment.

Contribution

This work presents the first CNN-based multi-task approach for simultaneous surgical phase recognition and tool detection in laparoscopic videos, using only visual data.

Findings

EndoNet outperforms existing methods in phase recognition accuracy.

The multi-task CNN achieves state-of-the-art tool detection results.

The approach simplifies data collection by eliminating manual annotations.

Abstract

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, 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 visual features used 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 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. Extensive experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.