Ultrasound-guided biopsy tracking using data-driven needle identification in application to kidney

Ultrasound-guided biopsy needle identification is a crucial step in clinical treatment planning, but remains challenging due to the difficulty in data acquisition that includes the ultrasound speckle interference pattern and the presence of strong linear anatomical structure. This paper introduces a real-time needle tracking method for visualizing 2D needle shapes and trajectory during interventions. Based on observations of the needle dynamics within a small fraction of dynamic ultrasound images, the current needle placement was estimated: (1) A subspace based background suppression technique was used to identify points representing possible needle locations using the consecutive dynamic frames in a sliding-window fashion. and (2) a Hough transform was then used to filter out false positives and fit the remaining points on the Hough space. Evaluation on datasets from 16 subjects demonstrated that the proposed method produced high-quality needle-only images, significantly reducing the mean trajectory error to 1.89°and the tip position error to 5.1 mm, outperforming temporal subtraction (2.65°and 14.3 mm) and Gabor filtering (2.94°and 13.8 mm). The attention-based U-Net achieved a comparable mean trajectory angle error of 1.82°but yielded a higher mean tip position error of 8.2 mm. Qualitative and quantitative analyses consistently indicated that the proposed method offers enhanced accuracy and robustness across subjects compared to competing methods.