Motion-aware Needle Segmentation in Ultrasound Images

Raghavv Goel,Cecilia Morales,Manpreet Singh,Artur Dubrawski,John Galeotti,Howie Choset; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7886-7891

Segmenting a moving needle in ultrasound images is challenging due to the presence of artifacts noise and needle occlusion. This task becomes even more demanding in scenarios where data availability is limited. In this paper we present a novel approach for needle segmentation for 2D ultrasound that combines classical Kalman Filter (KF) techniques with data-driven learning incorporating both needle features and needle motion. Our method offers three key contributions. First we propose a compatible framework that seamlessly integrates into commonly used encoder-decoder style architectures. Second we demonstrate superior performance compared to recent state-of-the-art needle segmentation models using our novel convolutional neural network (CNN) based KF-inspired block achieving a 15% reduction in pixel-wise needle tip error and an 8% reduction in length error. Third to our knowledge we are the first to implement a learnable filter to incorporate non-linear needle motion for improving needle segmentation.