# Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images
## 4 Discussion and Conclusions
However, the Histogram method showed the accurate and effective detection of the needle, but it relies on the brightness of the image as the needle could not be easily detected where there are plenty of pixels with the highest intensity (which has a max value of 255). However, this condition can be controlled by the setting of the ultrasound scanner to expand the gray level of the image properly. The proposed method still has its limitations. During the experiment, time is needed for the data collection from the US scanner and optical tracking system, and the movement of the robot arm, which is affected by the scan length and scan interval. However, it is very hard to acquire the whole position of a long needle in one scan for any medical image sensor. Therefore, in the future, it is valuable to find a method to reduce the times of needle detection in order to reduce the time for the tip estimation. Moreover, when the robot arm moves with the US probe precisely, the tissue and needle have a possibility to be deformed by the probe motion. Hence, it is necessary to make the robot arm move smoothly as well as correctly on the surface of tissue. In addition, patient motion is the biggest uncertain problem, which leads to the failure of needle insertion and detection. In the proposed system, we use the 2D US scanner for the detection of the needle in various kinds of tissue. However, the 3D US scanner can also be used in this system. Although it has volume data and the detection method is different, the tracking method is able to be similar, as we also use the 3D positions for tracking in this study. Moreover, time is also needed for data collection and the movement of the robot arm, especially for the long needle, which is easily out of view-field of US volumes or images. Therefore, we use 2D US images in this study. In this paper, a method for tracking a long-curved needle from the 2D transverse US images and the tip estimation is represented and demonstrated with RUS. Ultrasound is first used to detect the cross section, with the probe moving along a long-flexible needle. Needle shape tracking method combined needle detection with Kalman filter by using 3D needle positions develops an accurate location and a robust tracking procedure. Needle shape is then constructed by using the curve fitting method and its tip position is estimated based on the former result. A histogram method is introduced to detect the needle in image processing, which can improve the needle localization despite the abrupt intensity changes. This new imaging approach is proposed based on the selection of numbers of pixels with a higher gray level, which can directly remove the lower gray level to highlight the needle. Results of the experiments suggest that the detection of the needle by the histogram method and Kalman Filter has high precision with minimum error 0.13 mm with a 1 mm scan interval in the phantom experiment and maximum error 1.35 mm with a 8 mm scan interval in the pork experiment. With the increase of the scan interval, the mean error would rise. Moreover, results showed that the estimation of the tip position is less than 1 mm within 4 mm scan intervals. We suggest choosing a 4 mm scan interval to balance the precision and scanning time to maximize efficiency. In the future, we would make the experiments of how long the scan length is the best length to estimate the needle tip. The proposed method would be a great assist to surgeons to locate the needle tip when they perform percutaneous insertion procedures with a long flexible needle, such as prostate brachytherapy. Conceptualization, M.Q.-H.M.; validation, Z.L. ; writing--original draft, Z.L. ; writing--review and editing, S.S. and L.L. This research was funded in part by the National Natural Science Foundation of China, grant number 61803123, and in part by the National Key R&D Program of China, grant number 2018 YFB1307700, and in part by the Natural Science Foundation of Guangdong Province, China, grant number 2018A030310565.