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13 March 2018 Machine learning-based colon deformation estimation method for colonoscope tracking
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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.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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