TY - GEN
T1 - Machine learning-based colon deformation estimation method for colonoscope tracking
AU - Oda, Masahiro
AU - Kitasaka, Takayuki
AU - Furukawa, Kazuhiro
AU - Miyahara, Ryoji
AU - Hirooka, Yoshiki
AU - Goto, Hidemi
AU - Navab, Nassir
AU - Mori, Kensaku
N1 - Funding Information:
Parts of this research were supported by the MEXT/JSPS KAKENHI Grant Numbers 25242047, 26108006, 17H00867, the JSPS Bilateral International Collaboration Grants, and the JST ACT-I (JPMJPR16U9).
Publisher Copyright:
© 2018 SPIE.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
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U2 - 10.1117/12.2293936
DO - 10.1117/12.2293936
M3 - Conference contribution
AN - SCOPUS:85050690500
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2018
A2 - Fei, Baowei
A2 - Webster, Robert J.
PB - SPIE
T2 - Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
Y2 - 12 February 2018 through 15 February 2018
ER -