A neural network approach for flexible needle tracking in ultrasound images using Kalman filter

Percutaneous surgical procedures depend on the precise positioning of the needle tip for effectiveness. Although, robustly locating the tip of the needle still represents a challenge, specially when flexible needles are used. An imaging technique widely used for this task is 2D ultrasound, however the low signal/noise ratio makes it difficult to apply conventional image processing techniques. In this work, we propose an alternative method for detecting the needle in an ultrasound image and tracking it during a complete insertion. The proposed method combines a Multilayer Perceptron network with a Kalman filter estimator for improving robustness. In preliminary experimental characterization, we acquired ultrasound images for creating the data set and evaluated the performance of the tracker with a complete insertion video. However the tracking performance is still far from optimal, the obtained results suggests the neural network approach to be a feasible alternative to this problem.