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) framework to align the style of handheld device
data to those of standard devices. The proposed TISA can directly infer
handheld device images without extra training 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. We further validate
TISA as the clinical model for automatic measurements of spinal curvature and
carotid intima-media thickness. The automatic measurements agree well with
manual measurements made by human experts and the measurement errors remain
within clinically acceptable ranges. We demonstrate the potential for TISA to
facilitate automatic diagnosis on handheld ultrasound devices and expedite
their eventual widespread use.

通过 Training-free Image Style Alignment (TISA) 框架，本研究旨在解决手持式超声设备在医学诊断中的使用局限性问题，通过样式对齐手持设备数据和标准设备数据，实现无需额外训练的手持设备图像推理，用于临床应用，提升医疗检测、分割任务以及脊柱曲率和颈动脉内膜厚度的自动测量，验证了 TISA 作为手持式超声设备的临床模型，加速其广泛使用和自动诊断的潜力展示。