Ultra-Wideband Wireless Capsule Endoscope Localization Based on a Hybrid One-Shot Learning and Trilateration Method

Abstract

Wireless capsule endoscopy (WCE) is a minimally invasive procedure that allows for the examination of the gastrointestinal tract using a small, swallowable capsule equipped with a camera. Accurate localization of wireless capsule endoscopes within the gastrointestinal (GI) tract is pivotal for effective medical interventions. We propose a novel hybrid localization method, combining one-shot learning and trilateration, addressing challenges in the complex in-body wireless channel. Results indicate improved accuracy, surpassing traditional methods. Using a modified Laura model with increased organ sizes, a shift to a 4-zone configuration reveals altered electromagnetic interactions in the GI tract. This modification allows us to simulate scenarios involving larger individuals, providing insights into the adaptability of our proposed technique to diverse anatomical conditions. Despite the shift, our method excels, showing adaptability with superior performance in 4 and 5 zones across diverse locations. Sensitivity to environmental conditions is emphasized, impacted by factors like organ size. Similarity in results between 4 and 5 zones underscores adaptability, extending to $45^{o}$ and $90^{o}$ scenarios. In summary, our hybrid approach represents a promising advancement, enhancing accuracy for wireless capsule endoscope localization in varied GI tract environments.

Keywords

• Gastrointestinal tract examination
• deep neural network
• Siamese neural network (SNN)
• minimally invasive procedure
• zone parameterization