Development of a Needle Deflection Detection System for a CT Guided Robot

Lena Guinot, Ryosuke Tsumura, Shun Inoue, Hiroyasu Iwata

研究成果: Conference contribution

抄録

The uncertainty and unpredictability regarding the occurrence of needle deflection during percutaneous puncture, especially when using very fine needles, can greatly complexify surgical tasks such as needle insertion in the lower abdomen. To avoid the increased risks induced by prolonged CT scan radiation exposure, this paper offers an alternative to the retrieval of needle tip position from CT scan images. In this method, the deflection of the needle is detected and reported in accordance with insertion force data as the needle is inserted into the bowel. This method relies on the use of a Gated Recurrent Unit based neural network to predict the occurrence and type of deflection met during the procedure depending on the intended path and tissue type to be punctured in order to reach the target (cancer tumor). This system accounts for the original angle of insertion of the needle. Results of final experiments returned a 100% true positive rate, signifying that in the eventuality of needle deflection, it would systematically have been predicted by the neural network.

本文言語English
ホスト出版物のタイトルProceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ34-38
ページ数5
ISBN(電子版)9781728166674
DOI
出版ステータスPublished - 2020 1
イベント2020 IEEE/SICE International Symposium on System Integration, SII 2020 - Honolulu, United States
継続期間: 2020 1 122020 1 15

出版物シリーズ

名前Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020

Conference

Conference2020 IEEE/SICE International Symposium on System Integration, SII 2020
国/地域United States
CityHonolulu
Period20/1/1220/1/15

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • 生体医工学
  • 制御およびシステム工学
  • 安全性、リスク、信頼性、品質管理
  • 制御と最適化
  • 器械工学

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