Real-time Needle Shape Prediction In Soft-Tissue Based On Image Segmentation And Particle Filtering
This paper proposes a real-time method to predict the shape of a exible needle inserted into soft tissue using Transrectal Ultrasound (TRUS) image segmentation and a nonholonomic bicycle model informed via particle filter. The needle location is tracked in TRUS images to capture the needle shape up to a specified depth. Through the use of a particle filter the tracked needle shape updates the parameters of a kinematic bicycle model to predict the shape of the entire needle after it is fully inserted. The method is verified in both ex-vivo beef phantom tissue and in-vivo clinical images, yielding an average tip prediction error of less that 0.5 mm in both the ex-vivo and in-vivo image sets with a peak processing time of less than 9.5 ms per image frame.