Percutaneous image-guided tumor ablation is a minimally invasive surgical procedure for the treatment of malignant tumors using a needle-shaped ablation probe. Automating the insertion of a needle by using a robot could increase the accuracy and decrease the execution time of the procedure. Extracting the needle tip position from the ultrasound (US) images is of paramount importance for verifying that the needle is not approaching any forbidden regions (e.g., major vessels and ribs), and could also be used as a direct feedback signal to the robot inserting the needle. A method for estimating the needle tip has previously been developed combining a modified Hough transform, image filters, and machine learning. This paper improves that method by introducing a dynamic selection of the region of interest in the US images and filtering the tracking results using either a Kalman filter or a particle filter. Experiments where a biopsy needle has been inserted into a phantom by a robot have been conducted, guided by an infrared tracking system. The proposed method has been accurately evaluated by comparing its estimations with the needle tip's positions manually detected by a physician in the US images. The results show a significant improvement in precision and more than 85% reduction of 95th percentile of the error compared with the previous automatic approaches. The method runs in real time with a frame rate of 35.4 frames/s. The increased robustness and accuracy can make our algorithm usable in autonomous surgical systems for needle insertion.
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