Biopsy Needle Segmentation using Deep Networks on inhomogeneous Ultrasound Images

In minimally invasive interventional surgery, ultrasound imaging is usually used to provide real-time feedback in order to obtain the best diagnostic results or realize treatment plans, so how to accurately obtain the position of the medical biopsy needle is a problem worthy of study. 2D ultrasound simulation images containing the medical biopsy needle are generated, and our images background is from the real breast ultrasound image. Based on the deep learning network, the images containing the medical biopsy needle are used to analyze the effectiveness of different networks for needle localization for the purpose of returning needle positions in non-uniform ultrasound images. The results show that attention U-Net performed best and can accurately reflect the real position of the medical biopsy needle. The IoU and Precision can reach 90.19% and 96.25%, and the Angular Error is 0.40°. Clinical Relevance- Based on the deep network, for 2D ultrasound images containing medical biopsy needle, the localization precision can reach 96.25% and the Angular Error is 0.40°.