1 research outputs found
Machine learning-based colon deformation estimation method for colonoscope tracking
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.Comment: Accepted paper for oral presentation at SPIE Medical Imaging 2018,
Houston, TX, US