TY - GEN
T1 - Development of a Needle Deflection Detection System for a CT Guided Robot
AU - Guinot, Lena
AU - Tsumura, Ryosuke
AU - Inoue, Shun
AU - Iwata, Hiroyasu
N1 - Funding Information:
ACKNOWLEDGMENT The authors wish to thank the Hasumi International Research Foundation and Japan Society for Promotion of Science for supporting the work. .
Funding Information:
*Resrach supported by Hasumi International Research Foundation.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - 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.
AB - 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.
KW - machine learning
KW - needle deflection
KW - robot assisted needle insertion
UR - http://www.scopus.com/inward/record.url?scp=85082601825&partnerID=8YFLogxK
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U2 - 10.1109/SII46433.2020.9026168
DO - 10.1109/SII46433.2020.9026168
M3 - Conference contribution
AN - SCOPUS:85082601825
T3 - Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020
SP - 34
EP - 38
BT - Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/SICE International Symposium on System Integration, SII 2020
Y2 - 12 January 2020 through 15 January 2020
ER -
