Motion Informed Needle Segmentation in Ultrasound Images

arXiv:2312.01239v3 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv availability cs.cv cs.lg data eess.iv images moving noise novel paper segmentation type