A Virtual Scanning Framework for Robotic Spinal Sonography with Automatic Real-time Recognition of Standard Views

Li，Keyu1; Xu，Yangxin1; Liu，Li1; Meng，Max Q.H.1,2,3

10.1109/EMBC46164.2021.9629703

2021

1557-170X

978-1-7281-1180-3

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

4574-4577

1-5 Nov. 2021

Mexico

Ultrasound (US) imaging is widely used to assist in the diagnosis and intervention of the spine, but the manual scanning process would bring heavy physical and cognitive burdens on the sonographers. Robotic US acquisitions can provide an alternative to the standard handheld technique to reduce operator workload and avoid direct patient contact. However, the real-time interpretation of the acquired images is rarely addressed in existing robotic US systems. Therefore, we envision a robotic system that can automatically scan the spine and search for the standard views like an expert sonographer. In this work, we propose a virtual scanning framework based on real-world US data acquired by a robotic system to simulate the autonomous robotic spinal sonography, and incorporate automatic real-time recognition of the standard views of the spine based on a multi-scale fusion approach and deep convolutional neural networks. Our method can accurately classify 96.71% of the standard views of the spine in the test set, and the simulated clinical application preliminarily demonstrates the potential of our method.

Robotic Ultrasound System Spinal Ultrasound Imaging Standard View Recognition Ultrasound Image Classification

其他

英语

EI

20220811670385

2-s2.0-85122549164

Scopus

1.Chinese University of Hong Kong,Department of Electronic Engineering,Hong Kong,Hong Kong
2.Southern University of Science and Technology,Department of Electronic and Electrical Engineering,Shenzhen,China
3.Shenzhen Research Institute of the Chinese University of Hong Kong,Shenzhen,China

Li，Keyu,Xu，Yangxin,Liu，Li,et al. A Virtual Scanning Framework for Robotic Spinal Sonography with Automatic Real-time Recognition of Standard Views[C],2021:4574-4577.