# Shape Completion and Real-Time Visualization in Robotic Ultrasound Spine Acquisitions

arXiv: https://arxiv.org/abs/2508.08923 · HTML: https://mlsanity.com/paper/2508.08923/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.13321/) (2020)
- [Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework](https://mlsanity.com/paper/2111.02167/) (2021)
- [Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"](https://mlsanity.com/paper/2002.11404/) (2020)
- [PRO-TIP: Phantom for RObust automatic ultrasound calibration by TIP detection](https://mlsanity.com/paper/2206.05962/) (2022)

## Built on by (most cited first, 2 of 2)

- [Ultrasound-Guided Real-Time Spinal Motion Visualization for Spinal Instability Assessment](https://mlsanity.com/paper/2602.12917/) (2026)
- [NOIR: Neural Operator mapping for Implicit Representations](https://mlsanity.com/paper/2603.13118/) (2026)

## Abstract (arXiv)

Ultrasound (US) imaging is increasingly used in spinal procedures due to its real-time, radiation-free capabilities; however, its effectiveness is hindered by shadowing artifacts that obscure deeper tissue structures. Traditional approaches, such as CT-to-US registration, incorporate anatomical information from preoperative CT scans to guide interventions, but they are limited by complex registration requirements, differences in spine curvature, and the need for recent CT imaging. Recent shape completion methods can offer an alternative by reconstructing spinal structures in US data, while being pretrained on large set of publicly available CT scans. However, these approaches are typically offline and have limited reproducibility. In this work, we introduce a novel integrated system that combines robotic ultrasound with real-time shape completion to enhance spinal visualization. Our robotic platform autonomously acquires US sweeps of the lumbar spine, extracts vertebral surfaces from ultrasound, and reconstructs the complete anatomy using a deep learning-based shape completion network. This framework provides interactive, real-time visualization with the capability to autonomously repeat scans and can enable navigation to target locations. This can contribute to better consistency, reproducibility, and understanding of the underlying anatomy. We validate our approach through quantitative experiments assessing shape completion accuracy and evaluations of multiple spine acquisition protocols on a phantom setup. Additionally, we present qualitative results of the visualization on a volunteer scan.