Machine learning-based colon deformation estimation method for colonoscope tracking

Masahiro Oda; Takayuki Kitasaka; Kazuhiro Furukawa M.D.; Ryoji Miyahara M.D.; Yoshiki Hirooka; Hidemi Goto M.D.; Nassir Navab; Kensaku Mori

doi:10.1117/12.2293936

13 March 2018 Machine learning-based colon deformation estimation method for colonoscope tracking

Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa M.D., Ryoji Miyahara M.D., Yoshiki Hirooka, Hidemi Goto M.D., Nassir Navab, Kensaku Mori

Author Affiliations +

Proceedings Volume 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling; 1057619 (2018) https://doi.org/10.1117/12.2293936
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

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Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa M.D., Ryoji Miyahara M.D., Yoshiki Hirooka, Hidemi Goto M.D., Nassir Navab, and Kensaku Mori "Machine learning-based colon deformation estimation method for colonoscope tracking", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057619 (13 March 2018); https://doi.org/10.1117/12.2293936

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