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.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Medical Imaging
Published
2024-12-25
DOI
https://doi.org/10.1109/tmi.2024.3522071
Citations
1
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Rick Siow Mong Goh, Huazhu Fu, Hongye Zeng, Rui Zheng et al.
1 citations
IEEE Transactions on Medical Imaging
Generative Adversarial Networks and Image Synthesis
0.25
article

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

Rick Siow Mong Goh, Huazhu Fu, Hongye Zeng, Rui Zheng, Yuchong Gao, Kang Zhou, Ke Zou, Zhihao Chen, Haibin Zhang, Meng Wang, Hongbo Chen, Chengcheng Zhu, Yongxin Liu, Chang Jiang
article en
1 citations

Abstract

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.

IEEE Transactions on Medical ImagingVol. 44(4)
Agency for Science, Technology and Research (SG), Beth Israel Deaconess Medical Center (US), Harvard University (US), Sun Yat-sen University (CN), Tianjin University (CN), Chinese University of Hong Kong (HK), University of Washington (US), Sichuan University (CN), Fudan University (CN), Institute of High Performance Computing (SG), ShanghaiTech University (CN), Zhongshan Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 43%
Generative Adversarial Networks and Image Synthesis
0.25
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.