I am neither especially clever nor especially gifted. I am only very, very curious.
I have much interest in research. Since second year in university, I have entered several labs and have done quite a few projects and research work.
My undergraduate research interest includes image processing, computer aided diagnosis, Traditional Chinese Medicine, complex network, electronics, data mining.
Now I have joined Department of Electrical and Computer Engineering, University of Washington, Seattle to continue my research on medical image processing. I am currently working on developing image processing and artificial intelligence tools to assist vascular image review and clinical diagnosis.
I would consider myself as both a medical researcher and technical engineer, so it would be easier to understand my contributions to both the medical and technical societies from two outlines.
Contributions to medical science
I am interested in researching on human vasculature and vascular diseases. As vascular diseases are among top death causes worldwide, such as stroke, but the current gap is we are lacking sufficient techniques to explore human vasculature. MRI is an ideal modality to have a better view of arteries (even wall) but at great cost of manual reading. By introducing image processing and machine learning techniques, I made many review demanding projects feasible and led to new discoveries in medical science.
1. iCafe for intracranial artery quantification and analysis: iCafe page (est reading time: 5 min)
Validated the feasibility of artery quantification from MRA in the MRI paper.
Developed iCafe to quantify intracranial arteries in the MRM paper.
Found vascular reduction through aging among elderly population in the NoA paper.
Identified vascular changes before and after carotid revascularization: ISMRM2019_Arizona.pdf
A novel approach to quantify and analyze peripheral arteries: ISMRM2019-pCafe.pdf
Explored the arterial collateralization differences on peripheral artery disease: ISMRM2019_PAD.pdf
Discovered reduced intracranial arteries might be an indicator for dementia: ISMRM2019_ACT.pdf
2. FRAPPE for popliteal vessel wall quantification and analysis: FRAPPE page (est reading time: 5 min)
Validated the feasibility of fully automated popliteal vessel wall analysis from MR knee scan in the MRM paper.
Segmented vessel wall on 3.5 million popliteal images from the OAI dataset and discovered wall thickening pattern. (under review)
Discovered the correlation between physical exercises and popliteal lumen diameters. (under review)
Innovations in technical developments
I have great interest in retrieving and understanding the information from images. And image processing and machine learning (especially deep learning) techniques are perfect tools to help my explorations. I am able to apply novel techniques to solve clinical problems, and keep improving the performance of techniques during applications.
1. Techniques to improve the performance of artery segmentation
Y-net, one of the earliest deep learning approaches for intracranial artery segmentation. BIBM paper
Tracklet refinement on artery detections from 3D images, then vessel wall segmentation in polar coordinate system. arXiv
Designed confidence scores from neural network predictions for segmentations. CIRCGEN paper
2. Techniques for machine learning based vascular disease diagnosis/screening
Automated image quality assessment from weighted patches. SPIE paper
Carotid lesion classification using deep learning. ISMRM abstract
Feature map analysis on popliteal plaques. ISMRM2020_plaque.pdf
Stenosis detection from multiplanar view ISMRM2020_stenosis.pdf
3. Techniques for automated intracranial arteries analysis
Artery refinement on challenging scans (infants). MICCAIW paper
Graph neural network labeling for intracranial arteries. Under review
The following chart is for my graduate school research.
The following chart is for my undergraduate research.
Main research experience
2013.10 ~ 2016.6 Research with Associate professor Huiliang Shang on projects about image processing, medical topics on Traditional Chinese medicine, robotic control, visible light positioning, circuit theory.
2013.10 ~ 2014.11 Guided by professor Bin Wang through The FDUROP project about image processing , an undergraduate research funding project on image processing, computer vision and pattern recognition.
2015.8 ~ 2016.6 Research with Professor Yuanyuan Wang and Yi Guo, doing my capstone project about medical image registration, a research using network structure and circuit simulation methods to perform image registration.
2016.6-2016.7 Research in Center for Biomedical Imaging Research, Tsinghua University with Huijun Chen. Quantitative analysis of intracranial arteries through aging.
Please see my Curriculum Vitae for more information.
Click subtopic links above to see articles about the progress and outcome of my research.
Email me if you have interest in detailed information about the paper.
Updated on April. 2020