Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery

Kurmann, Thomas Kevin; Màrquez Neila, Pablo; Du, Xiaofei; Fua, Pascal; Stoyanov, Danail; Wolf, Sebastian; Sznitman, Raphael (18 October 2017). Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Lecture Notes in Computer Science: Vol. 10434 (pp. 505-513). Springer 10.1007/978-3-319-66185-8_57

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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.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Image Guided Therapy > ARTORG Center - Ophthalmic Technology Lab
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Kurmann, Thomas Kevin; Màrquez Neila, Pablo; Wolf, Sebastian and Sznitman, Raphael

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISBN:

978-3-319-66184-1

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Raphael Sznitman

Date Deposited:

27 Feb 2018 16:26

Last Modified:

25 Oct 2019 23:56

Publisher DOI:

10.1007/978-3-319-66185-8_57

ArXiv ID:

1710.06668

BORIS DOI:

10.7892/boris.108437

URI:

https://boris.unibe.ch/id/eprint/108437

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