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. Convolutional Neural
Networks (CNNs) have been successful in many computer vision applications, but
struggle to accurately segment needles without considering their motion. In
this paper, we present a novel approach for needle segmentation that combines
classical Kalman Filter (KF) techniques with data-driven learning,
incorporating both needle features and needle motion. Our method offers two 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.

本文提出了一种通过将经典的 Kalman Filter (KF) 技术与数据驱动学习相结合的新方法来进行针头分割，旨在改进超声图像中的针头分割，提出的方法在常用的编码器 - 解码器结构中无缝集成，并利用基于卷积神经网络 (CNN) 的 KF-inspired 模块显著优于最近的先进针头分割模型，成功实现像素级针尖误差减少了 15% 和长度误差减少了 8% 的结果。