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Simultaneous recognition and pose estimation of instruments in minimally invasive surgery

Kurmann, T; Marquez Neila, P; Du, X; Fua, P; Stoyanov, D; Wolf, S; Sznitman, R; (2017) Simultaneous recognition and pose estimation of instruments in minimally invasive surgery. In: Descoteaux, M and Maier-Hein, L and Franz, A and Jannin, P and Collins, D and Duchesne, S, (eds.) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017. (pp. pp. 505-513). Springer: Cham. Green open access

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Abstract

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.

Type: Proceedings paper
Title: Simultaneous recognition and pose estimation of instruments in minimally invasive surgery
Event: Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017.
ISBN-13: 9783319661841
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-66185-8_57
Publisher version: http://doi.org/10.1007/978-3-319-66185-8_57
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1576315
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