EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

By Andru Putra Twinanda, Sherif Shehata, Didier Mutter, Jacques Marescaux, Michel de Mathelin, Nicolas Padoy. IEEE Transactions on Medical Imaging, 2016

1,145 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Oncology, Surgery

View Paper: https://www.semanticscholar.org/paper/44cadd24a58af15898e18e4450df5134fddd3c7b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. P. Heng
2. Luc Van Gool
3. Li Fei-Fei
4. Andrew Zisserman
5. Qi-Yan Dou
6. Trevor Darrell
7. Dacheng Tao
8. Xiaogang Wang

In Oncology

1. P. Heng
2. Hao Chen
3. Lequan Yu
4. Balázs Harangi

In Surgery

1. Gregory Hager
2. Russell H. Taylor

Citations in the corpus

• SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network (2018, methodology): “in [11].”
• Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks (2018, methodology): “A number of works address frame-level tool detection in laparoscopic surgical videos, including [6, 7, 8, 9], as a part of the 2016 M2CAI Tool Presence Detection Challenge which includes a m2cai16-tool dataset [10].”
• Surgical data science – from concepts toward clinical translation (2020, background): “EndoVis (28%), Cholec80 (Twinanda et al., 2017) (21%) and JIGSAWS (Gao et al.”