Training-free image style alignment for self-adapting domain shift on
  handheld ultrasound devices
Hongye Zeng Ke Zou Zhihao Chen Yuchong Gao Hongbo Chen Haibin Zhang Kang Zhou Meng Wang Rick Siow Mong Goh Yong Liu Chang Jiang Rui Zheng Huazhu Fu
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.