Training-Free Image Style Alignment for Domain Shift on Handheld Ultrasound Devices.

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) to align the style of handheld device data to those of standard devices. The proposed TISA eliminates the demand for source data, and can transform the image style while preserving spatial context during testing. Furthermore, our TISA avoids continuous updates to the pre-trained model compared to other test-time methods and is suited for clinical applications. We show that TISA performs better and more stably in medical detection and segmentation tasks for handheld device data than other test-time adaptation methods. We further validate TISA as the clinical model for automatic measurements of spinal curvature and carotid intima-media thickness, and the automatic measurements agree well with manual measurements made by human experts. We demonstrate the potential for TISA to facilitate automatic diagnosis on handheld ultrasound devices and expedite their eventual widespread use. Code is available at https://github.com/zenghy96/TISA.