VR-Caps: A Virtual Environment for Capsule Endoscopy

TL;DR

VR-Caps is a comprehensive virtual simulation platform for capsule endoscopy that enables development, testing, and validation of medical imaging algorithms using realistic synthetic data, bridging the gap between simulation and real-world applications.

Contribution

The paper introduces VR-Caps, a versatile virtual environment for simulating capsule endoscopy, supporting diverse tissue conditions, capsule designs, and magnetic control, facilitating algorithm development and validation.

Findings

Deep neural networks trained on VR-Caps data perform well on real medical data.

VR-Caps effectively simulates various tissue and capsule conditions for algorithm testing.

The platform aids in developing algorithms for coverage, trajectory, 3D mapping, and disease classification.

Abstract

Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions. The desired tasks for these systems include visual localization, depth estimation, 3D mapping, disease detection and segmentation, automated navigation, active control, path realization and optional therapeutic modules such as targeted drug delivery and biopsy sampling. Data-driven algorithms promise to enable many advanced functionalities for capsule endoscopes, but real-world data is challenging to obtain. Physically-realistic simulations providing synthetic data have emerged as a solution to the development of data-driven algorithms. In this work, we present a comprehensive simulation platform for capsule endoscopy operations and introduce VR-Caps, a virtual active capsule environment that simulates a range of normal and abnormal tissue conditions (e.g., inflated, dry, wet etc.) and varied organ types, capsule endoscope designs (e.g., mono, stereo, dual and 360{\deg}camera), and the type, number, strength, and placement of internal and external magnetic sources that enable active locomotion. VR-Caps makes it possible to both independently or jointly develop, optimize, and test medical imaging and analysis software for the current and next-generation endoscopic capsule systems. To validate this approach, we train state-of-the-art deep neural networks to accomplish various medical image analysis tasks using simulated data from VR-Caps and evaluate the performance of these models on real medical data. Results demonstrate the usefulness and effectiveness of the proposed virtual platform in developing algorithms that quantify fractional coverage, camera trajectory, 3D map reconstruction, and disease classification.