Online event classification for liver needle insertion based on force patterns

Inko Elgezua, Sangha Song, Yo Kobayashi, Masakatsu G. Fujie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Abstract In recent years percutaneous treatments for cancer have won momentum in the medical field. With it new needle insertion robots appeared to overcome the difficulties associated with needle insertion into soft tissue. At first, the main focus was to achieve high needle placement accuracy, however, the focus nowadays has shifted toward needle steering and patient specific needle tissue interaction. In this paper we present a classification method to detect the type of tissue being punctured in real time. The purpose of the proposed method is to detect particular events that can be used in a situational awareness agent. First,wewill introduce themethodology to create the statistical models used for classification, next, we prove the feasibility of the proposed classification method with experimental results and show that the proposed method hit a target even when tissue is deformed by analyzing needle insertion force patterns.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages1145-1157
Number of pages13
Volume302
ISBN (Print)9783319083377
DOIs
Publication statusPublished - 2016
Event13th International Conference on Intelligent Autonomous Systems, IAS 2014 - Padova, Italy
Duration: 2014 Jul 152014 Jul 18

Publication series

NameAdvances in Intelligent Systems and Computing
Volume302
ISSN (Print)21945357

Other

Other13th International Conference on Intelligent Autonomous Systems, IAS 2014
CountryItaly
CityPadova
Period14/7/1514/7/18

Keywords

  • Needle insertion robot
  • Situation awareness
  • Soft tissue modeling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Online event classification for liver needle insertion based on force patterns'. Together they form a unique fingerprint.

  • Cite this

    Elgezua, I., Song, S., Kobayashi, Y., & Fujie, M. G. (2016). Online event classification for liver needle insertion based on force patterns. In Advances in Intelligent Systems and Computing (Vol. 302, pp. 1145-1157). (Advances in Intelligent Systems and Computing; Vol. 302). Springer Verlag. https://doi.org/10.1007/978-3-319-08338-4_83