Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery

• Published in: arXiv.org
• Main Authors: Kurmann, Thomas, Pablo Marquez Neila, Du, Xiaofei, Fua, Pascal, Stoyanov, Danail, Wolf, Sebastian, Sznitman, Raphael
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 18.10.2017
• Subjects: Artificial neural networks; Computer Science - Computer Vision and Pattern Recognition; Laparoscopy; Mathematical models; Neural networks; Parameters; Recognition; Surgery; Surgical apparatus & instruments; Surgical instruments; Windows (intervals)
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1710.06668

Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion. An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation. This allows our approach to be relatively parameter free at test time and shows good performance for both instrument detection and tracking. We show that our approach surpasses state-of-the-art results on in-vivo retinal microsurgery image data, as well as ex-vivo laparoscopic sequences.