Needle Shape Estimation In Soft Tissue Based On Partial Ultrasound Image Observation
We propose a method to estimate the entire shape of a long flexible needle, suitable for a needle insertion assistant robot. This method bases its prediction on only a small segment of a needle, imaged via ultrasound, after insertion. An algorithm is developed that can segment a needle observed partially in ultrasound images and fully in camera images, returning a polynomial representation of the needle shape after RANSAC processing. These two needle shapes are then coregistered into the same coordinate system using affine transformation matrices. The polynomial corresponding to the partial needle observation in ultrasound images is used as the input to a needle-tissue interaction model that predicts the entire needle shape. The needle shape predicted by the model is compared to the segmented needle shape based on camera images to validate the proposed approach. The results show that the entire needle shape can be accurately predicted in tissues of varying stiffness based on observation of parts of the needle in an ultrasound image.