  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.

Computer Vision and Pattern Recognition;Machine Learning