论文标题
可穿戴斑块超声体积成像的特征聚集的时空脊柱表面估计
Feature-aggregated spatiotemporal spine surface estimation for wearable patch ultrasound volumetric imaging
论文作者
论文摘要
骨结构的明确鉴定对于超声引导的腰部干预至关重要，但是由于自我阴影的椎骨解剖结构的复杂形状以及周围软组织结构的广泛背景噪声，这可能是具有挑战性的。因此，我们建议使用类似斑块的可穿戴超声溶液从多个成像角度捕获反射骨表面，并创建3D骨表示以进行介入引导。在这项工作中，我们将通过使用B型图像和手工制作的过滤器的汇总特征图来介绍我们的方法来估计椎骨骨表面。通过我们提出的微型可穿戴“贴片”超声设备收集的脊柱幻影图像数据评估了这些方法，结果表明，可以通过有希望的精度来实现基线方法的显着改进。配备了这个表面估计框架，我们的可穿戴超声系统可以在增强现实环境中为临床医生提供直观，准确的介入指导。
Clear identification of bone structures is crucial for ultrasound-guided lumbar interventions, but it can be challenging due to the complex shapes of the self-shadowing vertebra anatomy and the extensive background speckle noise from the surrounding soft tissue structures. Therefore, we propose to use a patch-like wearable ultrasound solution to capture the reflective bone surfaces from multiple imaging angles and create 3D bone representations for interventional guidance. In this work, we will present our method for estimating the vertebra bone surfaces by using a spatiotemporal U-Net architecture learning from the B-Mode image and aggregated feature maps of hand-crafted filters. The methods are evaluated on spine phantom image data collected by our proposed miniaturized wearable "patch" ultrasound device, and the results show that a significant improvement on baseline method can be achieved with promising accuracy. Equipped with this surface estimation framework, our wearable ultrasound system can potentially provide intuitive and accurate interventional guidance for clinicians in augmented reality setting.
