A Realtime Needle Tracking Algorithm with Firstframe Linear Structure Removing in 2D Ultrasoundguided Prostate Therapy * In ultrasoundxe2x80x93guided percutaneous interventions, tracking needle tip is always difficult due to the existence of speckle artifact. Moreover, the complexity of prostate tissue increases the difficulties. In this paper, a realtime needle tracking algorithm is presented for localization and tracking of needlexe2x80x99s tip in 2D ultrasound images during robotic biopsy and brachytherapy. The proposed method uses image without needle as reference to detect needle insertion in an initial step, and this is followedup with removal of linear structure in surrounding tissue. Validation for needle localization was performed with 827 sequence frames observed in a prostate phantom. While, the shortest detectable needle length observed is 4.50 mm, shaft and tip localization errors are 1.04 xc2xb1 0.35 the degree and 0.75 xc2xb1 0.6 mm, respectively, and the least tip localization error was 0.06 mm. Meanwhile, a total processing time and single frame average processing time of 33 secs and 40 msec were achieved, respectively. Thus, our algorithm could track the needle tip in an acceptable tracking accuracy and time useful for robotic prostate therapy.

Ultrasound needle segmentation and trajectory prediction using excitation network Ultrasound (US)guided percutaneous kidney biopsy is a challenge for interventionists as US artefacts prevent accurate viewing of the biopsy needle tip. Automatic needle tracking and trajectory prediction can increase operator confidence in performing biopsies, reduce procedure time, minimize the risk of inadvertent biopsy bleedings, and enable future imageguided robotic procedures. In this paper, we propose a trackingbysegmentation model with spatial and channel xe2x80x9cSqueeze and Excitationxe2x80x9d (scSE) for US needle detection and trajectory prediction. We adopt a light deep learning architecture (e.g. LinkNet) as our segmentation baseline network and integrate the scSE module to learn spatial information for better prediction. The proposed model is trained with the US images of anonymized kidney biopsy clips from 8 patients. The contour is obtained using the borderfollowing algorithm and area calculated using Green formula. Trajectory prediction is made by extrapolating from the smallest bounding box that can capture the contour. We train and test our model on a total of 996 images extracted from 102 short videos at a rate of 3 frames per second from each video. A set of 794 images is used for training and 202 images for testing. Our model has achieved IOU of 41.01%, dice accuracy of 56.65%, F1score of 36.61%, and rootmeansquare angle error of 13.3circ . We are thus able to predict and extrapolate the trajectory of the biopsy needle with decent accuracy for interventionists to better perform biopsies. Our novel model combining LinkNet and scSE shows a promising result for kidney biopsy application, which implies potential to other similar ultrasoundguided biopsies that require needle tracking and trajectory prediction.