  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.
