Li Chen Curriculum Vitae


Email: cluw@uw.edu


RESEARCH INTERESTS

Medical Image Processing, Machine Learning, Computer Vision, Computer Aided Diagnosis, Traditional Chinese Medicine


ACADEMIC QUALIFICATIONS

Sep. 2016 – Now

Ph.D. Candidate in Electrical and Computer Engineering, University of Washington

Research assistant in Vascular Imaging Lab. Co-advised by Prof. Chun Yuan and Prof. Jenq-Neng Hwang
Expected to graduate in June 2021

Sep. 2012 – June 2016

B.S. in Electrical Engineering, Fudan University

Overall   GPA:  3.6 /4.0      Ranking:  9%

Jan. 2015 – May. 2015

Exchange Student in National University of Singapore

Overall   GPA:  5.0 /5.0


CONTRIBUTIONS TO SCIENCE

1. Vascular image analysis for artery structure and blood flow modeling

Modeling vascular regions shown in Magnetic Resonance Angiography (MRA) into an inter-connected tubular network (vasculature tree) is a novel image post-processing method to allow quantification of artery structures and blood flow. Quantitative measurements from the vasculature tree, including artery length, volume, tortuosity and signal intensity, provide novel imaging biomarkers for various research topics, such as evaluation of artery and flow differences in a certain population of interest, and identify vascular changes between time points. A series of research have been available since the development and validation of an artery feature extraction tool (iCafe, highlighted in Editor's Pick of the MRM journal). I have developed iCafe, validated its reliability on extracting features from TOF MRA, and applied iCafe on various interesting research projects, for example, exploration of cerebral vasculature decline for elderly in a cross-sectional study. iCafe can also be applied on other imaging sequences (CTA) or vascular beds (peripheral arteries).

2. Machine learning for medical research and clinical application

The powerful ability of machine learning techniques, especially deep learning techniques are bringing great benefits to medical solutions. I am actively taking the role of bridging the gap between technical and medical society by learning the clinical need and applying machine learning techniques to clinical applications. I have developed several clinically usable machine learning models to allow clinicians to easily use the advance techniques in their routine work or research. For example, I have developed an automated lumen and outer wall segmentation model using deep learning, which takes 5% of manual review time while having similar performance with expert readers. With that, we can process the whole OAI dataset within two months, a workload of 70 years of manual labeling, and thus we are able to analyze the vessel wall conditions in 3.5 million knee images. Another example is the high-risk atherosclerotic lesion detection model which is able to identify carotid artery centerlines and highlight the segments with high risk lesions, which is effective in reducing the manual review time and improve the detection sensitivity. Machine learning will definitely make tremendous contributions to the medical society in the future, and I am passionate to be involved in this exciting direction.

3. Image processing and computer vision for medical care

The image processing and computer vision techniques allow the computer to understand the content of images and reveal important information from signal intensities of medical images. For example, by using object tracking method artery of interest from 3D vascular images can be automatically identified, and an artery centerline can be generated for the following up visualization and vessel analysis. Another example is vascular image registration from two time points to observe vascular changes. Image features and patterns may contain additional information different from traditional clinical features, so that they can be utilized to identify subjects with health problems, such as identifying people with high risk plaques and detecting disease from tongue images. Applying these features in future clinical studies might establish a new approach for further our understanding of disease mechanism and prevention.


RESEARCH EXPERIENCES

Vascular Imaging Lab & Information Processing Lab

Sep. 2016 ~ Now

Research Assistant. Co-advised by Prof. Chun Yuan, Department of Radiology and Bioengineering, and Prof. Jenq-Neng Hwang, Department of Electrical and Computer Engineering

University of Washington

  • Quantitative intracranial artery modeling and vascular feature extraction

    • Developed an artery tracing and labeling tool (iCafe, C++ software, 60k lines) to model arteries and extract cerebral vascular features.
    • A novel artery refinement algorithm through optimization on Curved Planar Reformation view.
    • A novel artery naming algorithms using Graph Neural Network and hierarchical refinement.
    • Created a database of 1000+ scans of cerebral vasculature models.
    • Tool used by 12+ sites on dozens of medical research researches (aging, dementia, artery revascularization, etc.).
    • Three first-author journal papers (1,2,3) and seven conference publications ranging from technical development, validation and medical applications.
    • Editor's pick by Magnetic Resonance in Medicine.
    • iCafe website: http://icafe.clatfd.cn
  • Vessel wall segmentation using convolutional neural networks

    • Proposed an automated vessel wall segmentation and quantification method. (paper)
    • 3D region of interest identification using object tracking (Yolo V2 detector and tracklet refinement).
    • A novel vessel wall segmentation algorithm in polar coordinate system using deep learning.
    • Segmentation with uncertainty scores, proved to be useful in indicating segmentation performance.
    • Trained on more than one thousand subjects of labeled carotid vessel wall contours using convolutional neural network (CNN). Test set performance better than traditional cartesian based CNN methods (U-Net, Mask-RCNN).
  • Automated popliteal vessel wall segmentation and quantification.

    • Developed a fully-automated and robust vessel wall analysis tool (FRAPPE) to segment and quantify popliteal arteries and vessel walls for vascular research. (paper)
    • Processed on The Osteoarthritis Initiative (OAI) dataset with 3.5 Million popliteal artery images.
    • Transfer learning (from the carotid artery model) and active learning techniques to reduce labeling burden while maintaining accuracy.
    • Dice of 0.79 with human contours, only 1.2% images have major errors.
    • Found significant differences in vessel wall thickness measurements between high and low risk subjects.
    • Multi GPU process within 2 months, the workload of 70 years for an expert human reader.
    • Winner for American Heart Association/Amazon Web Services Prize Competition.
    • More technical details:http://clatfd.cn/a/286
  • Carotid artery atherosclerotic lesion screening using an AI based fully automated workflow based on 3D MRI

    • Proposed a 5-minute automated Magnetic Resonance screening workflow using multiple deep learning models.
    • 3D MERGE as the fast (2 minutes) MR imaging sequence.
    • Image quality assessment using target weighted patches. (joint work with Hongjian Jiang, master student under mentorship)
    • Multi slice multi channel patches for lesion classification.
    • High agreements with an expert radiologist (0.9+ sensitivity/specificity).
    • Assist radiologists by warning potential locations of advance lesions visualized in 3D view.
    • Effective in reducing manual review time for both experts and novice radiologists in clinical reading.
    • Online learning method to reduce labeling labors.
    • Patent filed.

Circuit Theory and Application Lab

Oct. 2013 ~ June 2016

Undergraduate Researcher. Advised by: Dr. Huiliang Shang, Associate Professor, Department of Electronic Engineering

Fudan University

  • Research: 'A Novel Automatic Tongue Image Segmentation Algorithm: Color Enhancement Method Based on L*a*b* Color Space'(published in Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on, First Author)

    • Utilize potential advantages of specific characteristics in three color-spaces to find a new method to detect tongue contour in the tongue diagnosis image.
    • In HSV space, using a hue threshold control function to determine a proper threshold value for preliminary separation of tongue region
    • In RGB space, performing color enhancement to obtain a better luminance which is more suitable for tongue segmentation
    • In L*A*B* space, using the luminance sensitivity of L channel to constrain the area of interest
    • Cooperated with Shanghai Traditional Chinese Medicine University and series of local hospitals
  • Research: 'The Application Characteristics of Traditional Chinese Medical Science Treatment on Vertigo Based on Data Mining Apriori Algorithm' (published on IJWMC, First Student Author)

    • To investigate statistical law in Traditional Chinese Medical(TCM) Treatment on vertigo using data mining technique
    • Created a database of TCM treatment prescriptions where 100 cases of prescriptions are selected.
    • Using Apriori algorithm for association rules mining in SPSS to find interrelationships among TCM syndromes, symptoms, and herbal medicine for vertigo, helping research on vertigo using TCM
    • Proposed a topology representation method using circuit simulation for vascular registration.
  • Research: 'An Adaptive Computer-aided Tongue Diagnosis Method using Color-calibration Preprocessing and Multiple Feature Synthesis based on Android' (published on IJWMC, First Student Author)

    • A research on diagnosing disease by taking a picture from tongue based on Android phone.
    • Accuracy and effectiveness corroborated with professional clinicians
    • Used QCGP to solve the problem of white balance
    • Adapted HSV model in Tongue brim pixels searching
    • Adapted inverted pentagonal four-line tongue outline searching and linking algorithm

Adaptive Networks and Control Lab (CAN)

Mar. 2014 ~ Mar. 2015

Undergraduate Researcher. Advised by: Prof. Xiang Li, Professor, Department of Electronic Engineering

Fudan University

  • Research: 'An improved method of acquaintance immunization strategy in complex network'(published on JTB (IF:2.116), First Author)

    • A research to find an improved method to effectively immune a virus spreading in complex network.
    • A new Index of NSI is presented to value the structure and importance of nodes in network
    • Improved the classical acquaintance immunization strategy using NSI to protect 14.9% more nodes and decrease the spread rate by 27.6% (compared to classical strategy in our simulation)
    • Simulated in various network structures. Best in most random graph compared to other immunization strategies in the perspective of maximum percentage of infected nodes

Key Laboratory of EMW information

Oct. 2013 ~ Nov. 2014

Undergraduate Researcher. Advised by: Prof. Bin Wang, Professor, Department of Electronic Engineering

Fudan University

  • FDUROP Project: 'The development of an inaccurate graph isomorphism algorithm and its application in prototype machine' (Exhibited on 2014 Shanghai Industrial Exhibition)

    • Building a prototype machine which uses contour extraction algorithms to determine the location of products in images captured from production line and sort them according to their shape.
    • Funded 6000 RMB by FuDan Undergraduate Research Opportunity Program(FDUROP)
    • Cooperated with Shanghai GO-WELL Electrical Technology CO.
    • Used Matlab and OpenCV for the algorithm and executed on multi-platform (PC, raspberry, DSP)
    • Endured noises and overlaps, high recognition of different products on the same production line

Fudan Physic Teaching Lab

Sep. 2013 ~ Dec. 2013

Undergraduate Researcher. Advised by: Prof. Yongkang Le, Professor, Department of Physics

Fudan University

  • Application: 'A remote & online electrical control center using infrared ray based on Arduino'

    • A versatile remote control center which can operate multiple electrical devices using infrared ray.
    • Emit infrared ray instructions under Internet control from remote devices (PC, smartphone, iPad)
    • Can memorize codes of up to 3 infrared control devices
    • A project for the Arduino contest held by Fudan Physic research center

For Whole Research Experience, Link to Project Journey


PUBLICATION

Journal Publications

  1. Li Chen, Huilin Zhao, Hongjian Jiang, Niranjan Balu, Hiroko Watase, Duygu Baylam Geleri, Xihai Zhao, Rui Li, Jianrong Xu, Thomas S. Hatsukami, Dongxiang Xu, Jenq-Neng Hwang, Chun Yuan. Carotid Artery Atherosclerotic Lesion Detection using an AI based Fully automated Workflow Based on 3D MRI. Under review, 2020, .

  2. Li Chen, Gador Canton, Wenjin Liu, Daniel S. Hippe, Niranjan Balu, Hiroko Watase, Thomas S. Hatsukami, John C. Waterton, Jenq-Neng Hwang, Chun Yuan. Fully automated and Robust Analysis Technique for Popliteal Artery Vessel Wall Evaluation (FRAPPE) using Neural Network Models from Standardized Knee MRI. Magnetic resonance in medicine, 2020, . DOI: 10.1002/mrm.28237

  3. Li Chen, Jie Sun, Gador Canton, Niranjan Balu, Daniel S. Hippe, Xihai Zhao, Rui Li, Thomas S. Hatsukami, Jenq-Neng Hwang, Chun Yuan. Automated Artery Localization and Vessel Wall Segmentation of Vessel Wall Images using Tracklet Refinement and Polar Conversion . Under review, 2020, .

  4. Li Chen, Stephen R. Dager, Dennis W.W. Shaw, Neva M. Corrigan, Mahmud Mossa-Basha, Kristi D. Pimentel, Natalia M. Kleinhans, Patricia K. Kuhl, Jenq-Neng Hwang, Chun Yuan. A Novel Algorithm for Refining Cerebral Vascular Measurements in Infants and Adults. Journal of Neuroscience Methods (IF:2.785), 2020, 340 (1 July 2020), Pages 108751. DOI: 10.1016/j.jneumeth.2020.108751

  5. Daniel Hippe, Niranjan Balu, Li Chen, Gador Canton, Wenjin Liu, Hiroko Watase, John Waterton, Thomas Hatsukami, Jenq-Neng Hwang, Chun Yuan. Confidence weighting for robust automated measurements of popliteal vessel wall MRI. Circulation: Genomic and Precision Medicine (IF:4.864), 2020, 13 (1), Pages 39-41. DOI: 10.1161/CIRCGEN.119.002870

  6. Li Chen, Mahmud Mossa-Basha, Jie Sun, Daniel S Hippe, Niranjan Balu, Quan Yuan, Kristi Pimentel, Thomas S. Hatsukami, Jenq-Neng Hwang, Chun Yuan*. Quantification of morphometry and intensity features of intracranial arteries from 3D TOF MRA using the intracranial artery feature extraction (iCafe): A reproducibility study. Magnetic Resonance Imaging (IF:2.1), 2019, 57 (April 2019), Pages 293-302. DOI: 10.1016/j.mri.2018.12.007

  7. Li Chen, Jie Sun, Daniel S Hippe, Niranjan Balu, Quan Yuan, Isabelle Yuan, Xihai Zhao, Rui Li, Le He, Thomas S. Hatsukami, Jenq-Neng Hwang, Chun Yuan. Quantitative Assessment of the Intracranial Vasculature in an Older Adult Population using iCafe (intraCranial Artery Feature Extraction). Neurobiology of Aging (IF:4.4), 2019, 79 (July 2019), Pages 59-65. DOI: 10.1016/j.neurobiolaging.2019.02.027

  8. Qiang Zhang, Zhensen Chen, Shuo Chen, Xinke Liu, Jia Ning, Yongjun Han, Li Chen, Le He, Xihai Zhao, Yuhui Xiong, Hua Guo, Chun Yuan, Rui Li, Huijun Chen. Angiographic contrast mechanism comparison between Simultaneous Non-contrast Angiography and intraPlaque hemorrhage (SNAP) sequence and Time of Flight (TOF) sequence for intracranial artery. Magnetic Resonance Imaging (IF:2.1), 2019, 66 (February 2020), Pages 199-207. DOI: 10.1016/j.mri.2019.09.001

  9. Li Chen, Mahmud Mossa‐Basha, Niranjan Balu, Gador Canton, Jie Sun, Kristi Pimentel, Thomas S Hatsukami, Jenq‐Neng Hwang, Chun Yuan. Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open‐curve active contour vessel tracing. Magnetic resonance in medicine (IF:3.9), 2018, 79 (6), Pages 3229-3238. DOI: 10.1002/mrm.26961

  10. Li Chen, Yuxi Lian, Yi Guo, Yuanyuan Wang, Thomas S Hatsukami, Kristi Pimentel, Niranjan Balu, Chun Yuan. A vascular image registration method based on network structure and circuit simulation. BMC bioinformatics (IF:2.5), 2017, 18 (1), Pages 229. DOI: 10.1186/s12859-017-1649-1

  11. Li Chen, Dongyi Wang. An improved acquaintance immunization strategy for complex network. Journal of theoretical biology, 2015,  (385), Pages 58-65. DOI: 10.1016/j.jtbi.2015.07.037

  12. Miao Wang, Li Chen, Qing Li, Dongyi Wang, Yiqin Liu, Yi Zhang, Shoulan Bing, Huiliang Shang. An adaptive computer-aided tongue diagnosis method using colour-calibration preprocessing and multiple feature synthesis based on Android platform. International Journal of Wireless and Mobile Computing, 2015, 9 (3), Pages 240-249. DOI: 10.1504/IJWMC.2015.073107

  13. Miao Wang, Li Chen, Yanjun Huang, Lei Zhang, Zihao Zhang, Jie Ding, Huiliang Shang. The application characteristics of traditional Chinese medical science treatment on vertigo based on data mining Apriori algorithm. International Journal of Wireless and Mobile Computing , 2015, 9 (4), Pages 349-354. DOI: 10.1504/IJWMC.2015.074041


Conference Publications

  1. Hongjian Jiang, Li Chen, Dongxiang Xu, Huilin Zhao, Hiroko Watase, Xihai Zhao, Rui Li, Chun Yuan. A Target-Oriented and Multi-Patch Based Framework for Image Quality Assessment on Carotid Artery MRI. Medical Imaging 2020: Image Processing. SPIE, 2020, Marriott Marquis Houston, Houston, Texas, United States  (February 15 - 20). DOI: 10.1117/12.2549473

  2. Li Chen, Thomas Hatsukami, Jenq-Neng Hwang, Chun Yuan. Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement. submitted to MICCAI 2020, under review, 2020, .

  3. Li Chen, Gaoang Wang, Niranjan Balu, Mahmud Mossa-Basha, Xihai Zhao, Rui Li, LeHe, Thomas S. Hatsukami, Jenq-Neng Hwang, Chun Yuan. Simultaneous Intracranial Artery Tracing and Segmentation from Magnetic Resonance Angiography by Joint Optimization from Multiplanar Reformation. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting, First International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI, 2019, Shenzhen, China (October 13), Pages 201-209. DOI: 10.1007/978-3-030-33327-0

  4. Li Chen, Yanjun Xie, Jie Sun, Niranjan Balu, Mahmud Mossa-Basha, Kristi Pimentel, Thomas S Hatsukami, Jenq-Neng Hwang, Chun Yuan. 3D intracranial artery segmentation using a convolutional autoencoder. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017, Kansas City, MO, USA (November 13 - 16). DOI: 10.1109/BIBM.2017.8217741

  5. Li Chen, Dongyi Wang, Yiqin Liu, Xiaohang Gao, Huiliang Shang. A novel automatic tongue image segmentation algorithm: Color enhancement method based on L* a* b* color space. Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on, 2015, Washington D.C., USA ( November 9-12), Pages 990-993. DOI: 10.1109/BIBM.2015.7359818

  6. Miao Wang, Jiayun Li, Li Chen, Yanjun Huang, Qiang Zhou, Lijuan Che, Huiliang Shang. The Study of the Compatibility Rules of Traditional Chinese Medicine Based on Apriori and HMETIS Hypergraph Partitioning Algorithm. VLDB Workshop on Big Graphs Online Querying, 2015, Waikoloa, HI, USA (August 31 – September 4), Pages 16-31. DOI: 10.1007/978-3-319-41576-5_2


Conference/Symposium/Workshop Oral Presentation

  1. Li Chen, Wenjin Liu, Gador Canton, Niranjan Balu, Thomas Hatsukami, John C. Waterton, Jenq-Neng Hwang, Chun Yuan. Visualizing and utilizing the latent features of MR vessel wall images using weakly supervised deep learning analysis workflow. 2020 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2020, ICC Sydney, Sydney, Australia (17-20 April 2020).

  2. Li Chen. Image analysis and AI/deep learning applications on vascular research. Invited talk, 2019, Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China (October 9).

  3. Li Chen. Looking into intracranial artery map with big data and machine learning. Intelligent Medical Imaging Symposium, 2018, Tsinghua University, Beijing, China (July 1).

  4. Li Chen, Huilin Zhao, Niranjan Balu, Xihai Zhao, Rui Li, Jenq-Neng Hwang, Chun Yuan. Carotid Artery Localization and Lesion Detection on 3D-MERGE MRI through Online Learning.. Society for Magnetic Resonance Angiography 30th Annual International Conference, 2018, University of Glasgow, Glasgow, Scotland (August 29-31).

  5. Li Chen. Development of a Quantitative Intracranial Vascular Features Extraction Tool on 3D MRA Using Semi-automated Open-Curve Active Contour Vessel Tracing. UW-Philips MRX, 2017, University of Washington (September 22).

  6. Li Chen, Jie Sun, Niranjan Balu, Thomas S. Hatsukami, Mahmud MossaBasha, Kristi D. Pimentel, Jenq-Neng Hwang, Chun Yuan. Automated detection and labeling of the intracranial arterial tree in routine MR angiography: A machine learning approach enhanced with structural saliency. Radiological Society of North America's 103rd Scientific Assembly and Annual Meeting, 2017, McCormick Place, Chicago, Illinois (November 26 - December 1).


Preprints

  1. Li Chen, Jie Sun, Gador Canton, Niranjan Balu, Xihai Zhao, Rui Li, Thomas S. Hatsukami, Jenq-Neng Hwang, Chun Yuan. Automated Artery Localization and Vessel Wall Segmentation of Magnetic Resonance Vessel Wall Images using Tracklet Refinement and Polar Conversion. arXiv preprint, 2019,  (arXiv: 1909.02087).

  2. Li Chen, Yanjun Xie, Jie Sun, Niranjan Balu, Mahmud Mossa-Basha, Kristi Pimentel, Thomas S Hatsukami, Jenq-Neng Hwang, Chun Yuan. Y-net: 3D intracranial artery segmentation using a convolutional autoencoder. arXiv preprint, 2017,  (arXiv: 1712.07194).


Conference Posters/E-posters

  1. Li Chen, Duygu Baylam Geleri, Jie Sun, Hiroko Watase, Jiarui Cai, Yin Guo, Niranjan Balu, Dongxiang Xu, Thomas Hatsukami, Yongjun Wang, Jenq-Neng Hwang, Chun Yuan. Multi-planar, multi-contrast and multi-timepoint analysis tool (MOCHA) for intracranial vessel wall imaging review. 2020 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2020, ICC Sydney, Sydney, Australia (17-20 April 2020).

  2. Ke Han, Li Chen, Duygu Baylam Geleri, Mahmud Mossa-basha, Thomas Hatsukami, Chun Yuan. Deep-learning based Significant Stenosis detection from Multiplanar reformatted Images of traced Intracranial arteries. American Society of Neuroradiology 58th Annual Meeting, 2020, Las Vegas (May 30 – June 4).

  3. Gador Canton, Hiroko Watase, Josh Liu, Li Chen, Yin Guo, Yongjun Wang, Chun Yuan. Multiplanar reconstruction of intracranial arteries: luminal morphological characterization of atherosclerosis. 2020 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2020, ICC Sydney, Sydney, Australia (17-20 April 2020).

  4. Li Chen, Huilin Zhao, Niranjan Balu, Xihai Zhao, Rui Li, Jianrong Xu, Thomas S Hatsukami, Jenq-Neng Hwang, Chun Yuan. Carotid Artery Localization and Lesion Classification on 3D-MERGE MRI using Neural Network and Object Tracking methods. 2019 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2019, Palais des congrès de Montréal, Montréal, QC, Canada (May 11-16).

  5. Li Chen, Thomas Grabowski, Eric B Larson, Paul Crane, Thomas S Hatsukami, Jenq-Neng Hwang, Chun Yuan, Niranjan Balu. Quantitative Intracranial Vasculature Assessment to detect dementia using the intraCranial Artery Feature Extraction (iCafe) Technique. 2019 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2019, Palais des congrès de Montréal, Montréal, QC, Canada (May 11-16).

  6. Li Chen, Thoetphum Benyakorn, Gador Canton, Niranjan Balu, Thomas S Hatsukami, Jenq-Neng Hwang, Chun Yuan. Development of a Quantitative Assessment tool for Peripheral Artery Feature Extraction (pCafe). 2019 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2019, Palais des congrès de Montréal, Montréal, QC, Canada (May 11-16).

  7. Manabu Shirakawa, Li Chen, Niranjan Balu, Wenjin Liu, Dakota Ortega, Jinmei Chen, Theodore Trouard, Diane Bock, Wei Zhou, Chun Yuan, Thomas S Hatsukami. Quantitative assessment of cerebrovascular structure after carotid revascularization using intraCranial Artery Feature Extraction (iCafe) Technique. 2019 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2019, Palais des congrès de Montréal, Montréal, QC, Canada (May 11-16).

  8. Niranjan Balu, Li Chen, Thoetphum Benyakorn, Daniel S Hippe, Henrik Haraldsson, Warren Gasper, David Saloner, Chun Yuan, Thomas Hatsukami. Quantitative measurements of decreased arterial collateralization and branching in peripheral artery disease. 2019 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2019, Palais des congrès de Montréal, Montréal, QC, Canada (May 11-16).

  9. Li Chen, Mahmud Mossa-Basha, Daniel S Hippe, Jie Sun, Niranjan Balu, Kristi D. Pimentel, Thomas S Hatsukami, Jenq-Neng Hwang, Chun Yuan. Quantification of Morphometry and Intensity Features of Intracranial Arteries from 3D TOF MRA: A Reproducibility Study. 2018 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2018, Paris Expo Porte de Versailles, Paris, France (June 16-21).

  10. Li Chen, Quan Yuan, Niranjan Balu, Isabelle Yuan, Daniel S Hippe, Jie Sun, Xihai Zhao, Rui Li, Le He, Jenq-Neng Hwang, Chun Yuan. Quantitative Assessment of the Intracranial Vasculature of an Elderly Population using the intraCranial Artery Feature Extraction (iCafe) Technique. 2018 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2018, Paris Expo Porte de Versailles, Paris, France (June 16-21).

  11. Li Chen, Jie Sun, Wei Zhang, Thomas S Hatsukami, Jianrong Xu, Jenq-Neng Hwang, Chun Yuan. Automatic Segmentation of Carotid Vessel Wall Using Convolutional Neural Network. 2018 Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), 2018, Paris Expo Porte de Versailles, Paris, France (June 16-21).


AWARDS

2007-2009 Shanghai AVR single chip   programming contest (First prize in all three years)
2009,   2011 "INTEL" Innovation Contest in Shanghai
2009 Future Engineering Contest in Shanghai
2009-2015 Six consecutive years of Shu Ping Scholarship(top 3%)
2012 Fudan University Excellent Freshman Scholarship(top 5%)
2013-2014 Two consecutive years of Fudan university scholarship(top 15%)
2015 Mathematical Contest in Modeling 2015 (Meritorious Winner, top 10%)
2018 GSFEI Travel Funding, University of Washington
2018 Travel Stipend Awards, SMRA
2018 Outstanding Research Award, OCSMRM
2018, 2019 ISMRM Annual Meeting & Exhibition Stipend
2019 Magna Cum Laude Merit Award, ISMRM


ADVISORS

Chun Yuan

Professor — Department of Radiology and BioengineeringUniversity of Washington,
From 2016.9 — now

Researches on Magnetic Resonance Imaging, Vulnerable Plaque/Vessel Wall Imaging and Analysis, Cardiovascular Disease Analysis and Investigation, Vessel Imaging of Vascular Cognitive Impairment and Dementia, MRI Sequence Development and Image Reconstruction, Vessel Image Processing With AI, Machine Learning, and Deep Learning.

Email: cyuan@uw.edu

Experience with me:

             Course: MRI & ULTRASOUND
             PhD thesis co-chair.

Jenq-Neng Hwang

Professor — Department of Electrical & Computer Engineering University of Washington,
From 2016.9 — now

Researches on Image/video signal processing, Multimedia network and QoS, Statistical pattern recognition.

Email: hwang@uw.edu

Experience with me:

             PhD thesis co-chair.

Huiliang Shang

Accosiate Professor — Department of Electronic Engineering Fudan University,
From 2013.10 — 2016.6

Researches on image processing, Biomedical topics, Traditional Chinese medicine, Robotic, Computer vision, visible light communication, Circuit theory.

Email: shanghl@fudan.edu.cn

Experience with me:

             Course: Circuit Theory
             Four papers about Biomedical Image processing
             LED position car etc.

Bin Wang

Professor — Department of Electronic EngineeringFudan University
From 2013.10 — 2014.11

Researches on image processing, signal, pattern recongnition, intelligent information proessing and brain science

Email: wangbin@fudan.edu.cn

Experience with me:

             Course: Probability

             The FDUROP project about image processing

Xiang Li

Professor — Department of Electronic EngineeringFudan University
From 2014.3 — 2015.3

Researches on complex network and multi-agent systems.

Email: lix@fudan.edu.cn

Experience with me:

             Course: Introduction to Network Science

             Paper about immunization network

Yi Guo

Professor — Department of Electronic Engineering , Fudan University,
From 2015.8 — 2016.6

Researches on Medical image and signal processing, Medical ultrasonics.

Email: guoyi@fudan.edu.cn

Experience with me:

             Course: Medical Imaging
             Capstone research advisor
             One paper on vascualr registration

Yuanyuan Wang

Professor — Department of Electronic Engineering , Fudan University,
From 2015.8 — 2016.6

Researches on Medical image and signal processing, Medical ultrasonics.

Email: yywang@fudan.edu.cn

Experience with me:

             Course: Signal and System
             Final year project research group advisor
             



ACTIVITIES

2012-2013 Volunteer teaching in Shanghai Sunflower Weekend School, English Junior class.
2012-2014 Fudan Student Union, Department of Information. Technical director in 2014.
2014.7 Volunteer teaching in Yunnan. Lecturer in Paper cutting and Handwork (Build a gravity toy car)
2014-2015 Technical group of Shu Ping Scholarship Foundation. Devoloped online scholarship application system.
2015- IEEE student member.
2017- Trainee Member, International Society for Magnetic Resonance in Medicine (ISMRM).



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