Using augmentation to improve the robustness to rotation of deep learning segmentation in robotic-assisted surgical data

Danit Itzkovich, Yarden Sharon, Anthony Jarc, Yael Refaely, Ilana Nisky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Robotic-Assisted Minimally Invasive Surgery allows for easy recording of kinematic data, and presents excellent opportunities for data-intensive approaches to assessment of surgical skill, system design, and automation of procedures. However, typical surgical cases result in long data streams, and therefore, automated segmentation into gestures is important. The public release of the JIGSAWS dataset allowed for developing and benchmarking data-intensive segmentation algorithms. However, this dataset is small and the gestures are similar in their structure and directions. This may limit the generalization of the algorithms to real surgical data that are characterized by movements in arbitrary directions. In this paper, we use a recurrent neural network to segment a suturing task, and demonstrate one such generalization problem-limited generalization to rotation. We propose a simple augmentation that can solve this problem without collecting new data, and demonstrate its benefit using: (1) the JIGSAWS dataset, and (2) a new dataset that we recorded with a da Vinci Research Kit. Our study highlights the prospect of using data augmentation in the analysis of kinematic data in surgical data science.

Original languageEnglish
Title of host publication2019 International Conference on Robotics and Automation, ICRA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5068-5075
Number of pages8
ISBN (Electronic)9781538660263
DOIs
StatePublished - 1 May 2019
Event2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada
Duration: 20 May 201924 May 2019

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2019-May
ISSN (Print)1050-4729

Conference

Conference2019 International Conference on Robotics and Automation, ICRA 2019
Country/TerritoryCanada
CityMontreal
Period20/05/1924/05/19

Keywords

  • Deep learning in robotics and automation
  • Network generalization
  • Rotation augmentation
  • Surgical robotics: Laparoscopy

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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