Visual Localization of Wireless Capsule Endoscopes Aided by Artificial Neural Networks

Abstract

Various modalities are used for the examination of the gastrointestinal (GI) tract. One such modality is Wireless Capsule Endoscopy (WCE), a non-invasive technique which consists of a swallowable color camera that enables the detection of GI pathology with only minimal patient discomfort. Currently, tracking of the capsule position is estimated in the 3D abdominal space, using radio-frequency (RF) triangulation. The RF triangulation technique, however, does not provide sufficient information about the location of the capsule along the GI lumen, and consequently, the localization of any possible abnormality. Recently, we proposed a geometric visual odometry (VO) method for the localization of the capsule in the GI lumen. In this paper, we extend this state-of-art method by exploiting an artificial neural network (ANN) to augment the geometric method and achieve higher localization accuracy. The results of this novel approach are validated with an in-vitro experiment that provides ground truth information about the location of the capsule. The mean absolute error obtained, for a distance of 19.6cm, is 0.79±0.51cm.