Image-based laparoscopic tool detection and tracking using convolutional neural networks: a review of the literature. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Yang, Congmin; Zhao, Zijian; Hu, Sanyuan
• Is Part Of: Computer assisted surgery. Volume 25:Issue 1(2020)
• Page Start: 15
• Page End: 28
• Publication Date: 2020-01-01
• Subjects: Tool detection -- tool tracking -- convolutional neural network -- laparoscopic surgery; Computer-assisted surgery -- Periodicals
• DOI: 10.1080/24699322.2020.1801842
• Languages: English
• ISSNs: 2469-9322

Abstract

Abstract: Intraoperative detection and tracking of minimally invasive instruments is a prerequisite for computer- and robotic-assisted surgery. Since additional hardware, such as tracking systems or the robot encoders, are cumbersome and lack accuracy, surgical vision is evolving as a promising technique to detect and track the instruments using only endoscopic images. The present paper presents a review of the literature regarding image-based laparoscopic tool detection and tracking using convolutional neural networks (CNNs) and consists of four primary parts: (1) fundamentals of CNN; (2) public datasets; (3) CNN-based methods for the detection and tracking of laparoscopic instruments; and (4) discussion and conclusion. To help researchers quickly understand the various existing CNN-based algorithms, some basic information and a quantitative estimation of several performances are analyzed and compared from the perspective of 'partial CNN approaches' and 'full CNN approaches'. Moreover, we highlight the challenges related to research of CNN-based detection algorithms and provide possible future developmental directions.