Feature-aggregated spatiotemporal spine surface estimation for wearable patch ultrasound volumetric imaging
This work addresses the problem of accurate bone surface estimation for clinicians performing lumbar interventions, but it is incremental as it builds on existing U-Net architectures with feature aggregation.
The paper tackled the challenge of identifying bone structures in ultrasound-guided lumbar interventions by proposing a wearable patch ultrasound system with a spatiotemporal U-Net method, achieving significant improvement over baseline methods with promising accuracy.
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.