Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning

Abstract

This work presents an algorithm based on weak supervision to automatically localize an arthroscope on 3D ultrasound (US). The ultimate goal of this application is to combine 3D US with the 2D arthroscope view during knee arthroscopy, to provide the surgeon with a comprehensive view of the surgical site. The implemented algorithm consisted of a weakly supervised neural network, which was trained on 2D US images of different phantoms mimicking the imaging conditions during knee arthroscopy. Image-based classification was performed and the resulting class activation maps were used to localize the arthroscope. The localization performance was evaluated visually by three expert reviewers and by the calculation of objective metrics. Finally, the algorithm was also tested on a human cadaver knee. The algorithm achieved an average classification accuracy of 88.6% on phantom data and 83.3% on cadaver data. The localization of the arthroscope based on the class activation maps was correct in 92–100% of all true positive classifications for both phantom and cadaver data. These results are relevant because they show feasibility of automatic arthroscope localization in 3D US volumes, which is paramount to combining multiple image modalities that are available during knee arthroscopies.

Keywords

• ultrasound
• knee arthroscopy
• deep learning
• weakly supervised
• localization