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

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.