TY - GEN
T1 - Colon Shape Estimation Method for Colonoscope Tracking Using Recurrent Neural Networks
AU - Oda, Masahiro
AU - Roth, Holger R.
AU - Kitasaka, Takayuki
AU - Furukawa, Kasuhiro
AU - Miyahara, Ryoji
AU - Hirooka, Yoshiki
AU - Goto, Hidemi
AU - Navab, Nassir
AU - Mori, Kensaku
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - We propose an estimation method using a recurrent neural network (RNN) of the colon’s shape where deformation was occurred by a colonoscope insertion. Colonoscope tracking or a navigation system that navigates physician to polyp positions is needed to reduce such complications as colon perforation. Previous tracking methods caused large tracking errors at the transverse and sigmoid colons because these areas largely deform during colonoscope insertion. Colon deformation should be taken into account in tracking processes. We propose a colon deformation estimation method using RNN and obtain the colonoscope shape from electromagnetic sensors during its insertion into the colon. This method obtains positional, directional, and an insertion length from the colonoscope shape. From its shape, we also calculate the relative features that represent the positional and directional relationships between two points on a colonoscope. Long short-term memory is used to estimate the current colon shape from the past transition of the features of the colonoscope shape. We performed colon shape estimation in a phantom study and correctly estimated the colon shapes during colonoscope insertion with 12.39 (mm) estimation error.
AB - We propose an estimation method using a recurrent neural network (RNN) of the colon’s shape where deformation was occurred by a colonoscope insertion. Colonoscope tracking or a navigation system that navigates physician to polyp positions is needed to reduce such complications as colon perforation. Previous tracking methods caused large tracking errors at the transverse and sigmoid colons because these areas largely deform during colonoscope insertion. Colon deformation should be taken into account in tracking processes. We propose a colon deformation estimation method using RNN and obtain the colonoscope shape from electromagnetic sensors during its insertion into the colon. This method obtains positional, directional, and an insertion length from the colonoscope shape. From its shape, we also calculate the relative features that represent the positional and directional relationships between two points on a colonoscope. Long short-term memory is used to estimate the current colon shape from the past transition of the features of the colonoscope shape. We performed colon shape estimation in a phantom study and correctly estimated the colon shapes during colonoscope insertion with 12.39 (mm) estimation error.
UR - http://www.scopus.com/inward/record.url?scp=85053819013&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053819013&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00937-3_21
DO - 10.1007/978-3-030-00937-3_21
M3 - Conference contribution
AN - SCOPUS:85053819013
SN - 9783030009366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 176
EP - 184
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Fichtinger, Gabor
A2 - Schnabel, Julia A.
A2 - Alberola-López, Carlos
A2 - Davatzikos, Christos
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -