Machine learning-based colon deformation estimation method for colonoscope tracking

Oda M, Kitasaka T, Furukawa K, Miyahara R, Hirooka Y, Goto H, Navab N, Mori K (2018)

Publication Type: Conference contribution

Publication year: 2018

Journal

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Publisher: SPIE

Book Volume: 10576

Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Event location: Houston, TX, USA

ISBN: 9781510616417

DOI: 10.1117/12.2293936

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.

Involved external institutions

Aichi Institute of Technology (AIT) / 愛知工業大学 JP Japan (JP) Nagoya University / 名古屋大学 JP Japan (JP) Technische Universität München (TUM) DE Germany (DE) Nagoya University Hospital / 名古屋大学医学部附属病院 JP Japan (JP)

How to cite

APA:

Oda, M., Kitasaka, T., Furukawa, K., Miyahara, R., Hirooka, Y., Goto, H.,... Mori, K. (2018). Machine learning-based colon deformation estimation method for colonoscope tracking. In Baowei Fei, Robert J. Webster (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Houston, TX, USA: SPIE.

MLA:

Oda, Masahiro, et al. "Machine learning-based colon deformation estimation method for colonoscope tracking." Proceedings of the Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, Houston, TX, USA Ed. Baowei Fei, Robert J. Webster, SPIE, 2018.

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