# Feature-aggregated spatiotemporal spine surface estimation for wearable patch ultrasound volumetric imaging

arXiv: https://arxiv.org/abs/2211.05962 · HTML: https://mlsanity.com/paper/2211.05962/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Convolutional Gated Recurrent Networks for Video Segmentation](https://mlsanity.com/paper/1611.05435/) (2016)
- [Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN](https://mlsanity.com/paper/1806.09766/) (2018)
- [Automatic ultrasound vessel segmentation with deep spatiotemporal context learning](https://mlsanity.com/paper/2111.02461/) (2021)

## Abstract (arXiv)

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.