KST-Mixer: Kinematic Spatio-Temporal Data Mixer For Colon Shape Estimation

TL;DR

This paper introduces KST-Mixer, a novel method for estimating colon shape during colonoscope insertion by mixing kinematic data across space and time, significantly improving accuracy over previous approaches.

Contribution

The paper presents a new Kinematic Spatio-Temporal data Mixer (KST-Mixer) that effectively estimates colon shape by handling deformations during colonoscope insertion, outperforming prior methods.

Findings

Achieved 11.92 mm mean Euclidean distance error in phantom studies.

Significantly reduced shape estimation error compared to previous methods.

Demonstrated effectiveness of data mixing along spatial and temporal axes.

Abstract

We propose a spatio-temporal mixing kinematic data estimation method to estimate the shape of the colon with deformations caused by colonoscope insertion. Endoscope tracking or a navigation system that navigates physicians to target positions is needed to reduce such complications as organ perforations. Although many previous methods focused to track bronchoscopes and surgical endoscopes, few number of colonoscope tracking methods were proposed. This is because the colon largely deforms during colonoscope insertion. The deformation causes significant tracking errors. Colon deformation should be taken into account in the tracking process. We propose a colon shape estimation method using a Kinematic Spatio-Temporal data Mixer (KST-Mixer) that can be used during colonoscope insertions to the colon. Kinematic data of a colonoscope and the colon, including positions and directions of their centerlines, are obtained using electromagnetic and depth sensors. The proposed method separates the data into sub-groups along the spatial and temporal axes. The KST-Mixer extracts kinematic features and mix them along the spatial and temporal axes multiple times. We evaluated colon shape estimation accuracies in phantom studies. The proposed method achieved 11.92 mm mean Euclidean distance error, the smallest of the previous methods. Statistical analysis indicated that the proposed method significantly reduced the error compared to the previous methods.