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We propose an estimation method using a recurrent neural network (RNN) of thecolon's shape where deformation was occurred by a colonoscope insertion.Colonoscope tracking or a navigation system that navigates physician to polyppositions is needed to reduce such complications as colon perforation. Previoustracking methods caused large tracking errors at the transverse and sigmoidcolons because these areas largely deform during colonoscope insertion. Colondeformation should be taken into account in tracking processes. We propose acolon deformation estimation method using RNN and obtain the colonoscope shapefrom electromagnetic sensors during its insertion into the colon. This methodobtains positional, directional, and an insertion length from the colonoscopeshape. From its shape, we also calculate the relative features that representthe positional and directional relationships between two points on acolonoscope. Long short-term memory is used to estimate the current colon shapefrom the past transition of the features of the colonoscope shape. We performedcolon shape estimation in a phantom study and correctly estimated the colonshapes during colonoscope insertion with 12.39 (mm) estimation error.

Colon Shape Estimation Method for Colonoscope Tracking using Recurrent Neural Networks