@inproceedings{98bd6b42ad0441d0a4fb045529927c60,
title = "Machine learning-based colon deformation estimation method for colonoscope tracking",
abstract = "This paper presents a colon deformation estimation method, which can be used to estimate colon deformations during colonoscope insertions. Colonoscope tracking or navigation system that navigates a physician to polyp positions during a colonoscope insertion is required to reduce complications such as colon perforation. A previous colonoscope tracking method obtains a colonoscope position in the colon by registering a colonoscope shape and a colon shape. The colonoscope shape is obtained using an electromagnetic sensor, and the colon shape is obtained from a CT volume. However, large tracking errors were observed due to colon deformations occurred during colonoscope insertions. Such deformations make the registration difficult. Because the colon deformation is caused by a colonoscope, there is a strong relationship between the colon deformation and the colonoscope shape. An estimation method of colon deformations occur during colonoscope insertions is necessary to reduce tracking errors. We propose a colon deformation estimation method. This method is used to estimate a deformed colon shape from a colonoscope shape. We use the regression forests algorithm to estimate a deformed colon shape. The regression forests algorithm is trained using pairs of colon and colonoscope shapes, which contains deformations occur during colonoscope insertions. As a preliminary study, we utilized the method to estimate deformations of a colon phantom. In our experiments, the proposed method correctly estimated deformed colon phantom shapes.",
author = "Masahiro Oda and Takayuki Kitasaka and Kazuhiro Furukawa and Ryoji Miyahara and Yoshiki Hirooka and Hidemi Goto and Nassir Navab and Kensaku Mori",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 12-02-2018 Through 15-02-2018",
year = "2018",
doi = "10.1117/12.2293936",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Baowei Fei and Webster, {Robert J.}",
booktitle = "Medical Imaging 2018",
address = "United States",
}