Precise angle estimation of capsule robot in ultrasound using heatmap guided two-stage network

A capsule robot can be controlled inside gastrointestinal (GI) tract by an external permanent magnet outside of human body for finishing non-invasive diagnosis and treatment. Locomotion control of capsule robot relies on the precise angle feedback that can be achieved by ultrasound imaging. However, ultrasound-based angle estimation of capsule robot is interfered by gastric wall tissue and the mixture of air, water, and digestive matter existing in the stomach.

To tackle these issues, we introduce a heatmap guided two-stage network to detect the position and estimate the angle of the capsule robot in ultrasound images. Specifically, this network proposes the probability distribution module and skeleton extraction-based angle calculation to obtain accurate capsule robot position and angle estimation.

Extensive experiments were finished on the ultrasound image dataset of capsule robot within porcine stomach. Empirical results showed that our method obtained small position center error of 0.48 mm and high angle estimation accuracy of 96.32%.

Our method can provide precise angle feedback for locomotion control of capsule robot.