Qiyun Zhang ¹ Jiawei Chen ¹ Jinhong Wang ² Haolin Wang ³ Yi He ^4,5 Bin Li ⁶ Zhemin Zhuang ^1* Huancheng Zeng ^7*

• ¹ College of Engineering, Shantou University, Shantou, Guangdong, China
• ² Department of Ultrasound, Shantou Chaonan Minsheng Hospital, Shantou, Guangdong Province, China
• ³ Shantou University, Shantou, China
• ⁴ College of Medicine, Shantou University, Shantou, Guangdong Province, China
• ⁵ Department of Ultrasound, Shantou Central Hospital, Shantou, Guangdong Province, China
• ⁶ Product Development Department, Shantou Institute of Ultrasonic Instruments, Shantou, Guangdong Province, China
• ⁷ Cancer Hospital, College of Medicine, Shantou University, Shantou, Guangdong Province, China

Introduction: Ultrasound-guided needle biopsy is a commonly employed technique in modern medicine for obtaining tissue samples, such as those from breast tumors, for pathological analysis.However, it is limited by the low signal-to-noise ratio and the complex background of breast ultrasound imaging. In order to assist physicians in accurately performing needle biopsies on pathological tissues, minimize complications, and avoid damage to surrounding tissues, computeraided needle segmentation and tracking has garnered increasing attention, with notable progress made in recent years. Nevertheless, challenges remain, including poor ultrasound image quality, high computational resource requirements, and various needle shape. Methods: This study introduces a novel Spatio-Temporal Memory Network designed for ultrasoundguided breast tumor biopsy. The proposed network integrates a hybrid encoder that employs CNN-Transformer architectures, along with an optical flow estimation method. From the Ultrasound Imaging Department at the First Affiliated Hospital of Shantou University, we developed a real-time segmentation dataset specifically designed for ultrasound-guided needle puncture procedures in breast tumors, which includes ultrasound biopsy video data collected from 11 patients. Results: Experimental results demonstrate that this model significantly outperforms existing methods in improving the positioning accuracy of needle and enhancing the tracking stability. Specifically, the performance metrics of the proposed model is as follows: IoU is 0.731, Dice is 0.817, Precision is 0.863, Recall is 0.803, and F1 score is 0.832. By advancing the precision of needle localization, this model contributes to enhanced reliability in ultrasound-guided breast tumor biopsy, ultimately supporting safer and more effective clinical outcomes. Discussion: The model proposed in this paper demonstrates robust performance in the computeraided tracking and segmentation of biopsy needles in ultrasound imaging, specifically for ultrasoundguided breast tumor biopsy, offering dependable technical support for clinical procedures.