Deep Residual Learning for Instrument Segmentation in Robotic Surgery

Pakhomov D, Premachandran V, Allan M, Azizian M, Navab N (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer

Book Volume: 11861 LNCS

Pages Range: 566-573

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen, CHN

ISBN: 9783030326913

DOI: 10.1007/978-3-030-32692-0_65

Abstract

Detection, tracking, and pose estimation of surgical instruments provide critical information that can be used to correct inaccuracies in kinematic data in robotic-assisted surgery. Such information can be used for various purposes including integration of pre- and intra-operative images into the endoscopic view. In some cases, automatic segmentation of surgical instruments is a crucial step towards full instrument pose estimation but it can also be solely used to improve user interactions with the robotic system. In our work we focus on binary instrument segmentation, where the objective is to label every pixel as instrument or background and instrument part segmentation, where different semantically separate parts of the instrument are labeled. We improve upon previous work by leveraging recent techniques such as deep residual learning and dilated convolutions and advance both binary-segmentation and instrument part segmentation performance on the EndoVis 2017 Robotic Instruments dataset. The source code for the experiments reported in the paper has been made public (https://github.com/warmspringwinds/pytorch-segmentation-detection ).

Involved external institutions

Johns Hopkins University (JHU) US United States (USA) (US) Intuitive Surgical, Inc. US United States (USA) (US)

How to cite

APA:

Pakhomov, D., Premachandran, V., Allan, M., Azizian, M., & Navab, N. (2019). Deep Residual Learning for Instrument Segmentation in Robotic Surgery. In Heung-Il Suk, Mingxia Liu, Chunfeng Lian, Pingkun Yan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 566-573). Shenzhen, CHN: Springer.

MLA:

Pakhomov, Daniil, et al. "Deep Residual Learning for Instrument Segmentation in Robotic Surgery." Proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Heung-Il Suk, Mingxia Liu, Chunfeng Lian, Pingkun Yan, Springer, 2019. 566-573.

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