Colon Shape Estimation Method for Colonoscope Tracking Using Recurrent Neural Networks
Oda M, Roth HR, Kitasaka T, Furukawa K, Miyahara R, Hirooka Y, Goto H, Navab N, Mori K (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11073 LNCS
Pages Range: 176-184
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030009366
DOI: 10.1007/978-3-030-00937-3_21
Abstract
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.
Involved external institutions
Nagoya University / 名古屋大学
Japan (JP)
Aichi Institute of Technology (AIT) / 愛知工業大学
Japan (JP)
Nagoya University Hospital / 名古屋大学医学部附属病院
Japan (JP)
Technische Universität München (TUM)
Germany (DE)
Japan (JP)
Aichi Institute of Technology (AIT) / 愛知工業大学
Japan (JP)
Nagoya University Hospital / 名古屋大学医学部附属病院
Japan (JP)
Technische Universität München (TUM)
Germany (DE)
How to cite
APA:
Oda, M., Roth, H.R., Kitasaka, T., Furukawa, K., Miyahara, R., Hirooka, Y.,... Mori, K. (2018). Colon Shape Estimation Method for Colonoscope Tracking Using Recurrent Neural Networks. In Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 176-184). Granada, ESP: Springer Verlag.
MLA:
Oda, Masahiro, et al. "Colon Shape Estimation Method for Colonoscope Tracking Using Recurrent Neural Networks." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos, Springer Verlag, 2018. 176-184.
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