News: Postdoctoral and visiting student/scholar positions

Li Wang is working in the University of North Carolina at Chapel Hill, USA. He works with Professor Dinggang Shen in Medical Image Analysis field. His research interests focus on image segmentation, image registration, cortical surface analysis, machine learning and their applications to normal early brain development and disorders. His currently focus is on the neonatal brain image segmentation and serial infant brain images segmentation.
Contact:
li.wang8401@gmail.com
li_wang@med.unc.edu


Job Opportunity

Postdoctoral and visiting student/scholar positions -- Early Brain Biomarker

Postdoctoral research associate and visiting student/scholar positions are available in the Department of Radiology and Biomedical Research Imaging Center (BRIC) at the University of North Carolina at Chapel Hill (UNC-Chapel Hill). Our current focuses are to identify early brain biomarker on abnormal brain development.

The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on image feature learning and segmentation. Experience on medical image segmentation using deep learning and shape statistics is highly desirable. People with machine learning background on medical imaging analysis are particularly encouraged to apply. Strong knowledge on programming (good command of LINUX, C and C++, scripting, and Matlab) is desirable. The research topic will be the development and validation of tissue segmentation and ROI labeling methods for infant brain images with risk of abnormal brain development, such as typical control subjects from our recently awarded Baby Connectome Project (BCP) as well as with-risk subjects from publicly dataset.

If interested, please email resume to Dr. Li Wang (li_wang@med.unc.edu).

Call for MICCAI Grand Challenge on 6-month infant brain Segmentation (iSeg-2017)

MICCAI Grand Challenge on 6-month infant brian Segmentation

Awards, Grants & Honors


Research & Source Codes

  • LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images. This novel method employs the random forest and auto-context model. [PDF] [PPT] [Email request for the code]
  • Neonatal Brain MR Image Segmentation using Sparse Representation and Patch-Driven Level Sets, Neuroimage, 84, 141-158, 2014. [PDF][PPT][[Email request for the code]
  • Longitudinally guided level sets for consistent tissue segmentation of neonates, Human Brain Mapping, 34(4), 956-972, 2013.[PDF][BibTex][Infant processing software].
  • Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Sets, NeuroImage, 58:805-817, 2011. [PDF][Infant processing software]
  • Medical Image Segmentation with Local Gaussian Distribution (LGD) Fitting Energy [PDF][Matlab Source Code]


>>>>> LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images. This novel method employs the random forest and Haar-like context model. [Journal version] [PPT] [Email request for the code]

The proposed method can be applied on brain images at any time phase of lifespan, from 2-weeks neonatal brain to elderly brain. Especially, our method has achieved great performances on both neonatal (< 3 months of age) brain segmentation MICCAI challenge and MR brain (> 12 months of age) segmentation MICCAI challenge.

The following shows the LINKS framework for the training stage and application stage on the isointense (~6 months old of age) infant brain images. Such isointense infant brain images have an extramely low tissue contrast due to myelination, which results in a very challengeing task for the segmentation:

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

We have applied this framework on the MRBrainS13 MICCAI Challenge data. The following shows the segmentation results on a target brain image by our method. Our model achieves the best results of WM in terms of Dice coefficient and Hausdorff distance, GM in terms of Hausdorff distance, Intracranial cavity in terms of Absolute Volume Difference. For more info, please refer to the MICCAI MRBrainS13 Challenge.

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images
LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images




We also have applied this framework on the NeoBrainS12 MICCAI Challenge data. The following shows the segmentation results on 3 target neonatal images by our method. Our model achieves the best results of WM (UWM+MWM), CoGM and BGT in terms of all measurements (Dice coefficient, mean surface distance and Hausdorff distance). For more info, please refer to the NeoBrainS12 Challenge.

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images



>>>>> Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation. [PDF] [Journal version] [Code will be released soon]

Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation

Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation




>>>>> Neonatal Brain MR Image Segmentation using Sparse Representation and Patch-Driven Level Sets. [PDF][PPT][Matlab Source Code will be available]

Neonatal Brain MR Image Segmentation using Patch-Driven Level Sets




>>>>> Longitudinally guided level sets for consistent tissue segmentation of neonates. [PDF] [Infant processing software]

neonatal image segmentation neonate brain segmentation longitudinal image




>>>>> Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Sets. [PDF][Infant processing software]

Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Sets




>>>>> Extraction of the Cerebral Cortical Boundaries (TBA) (click the image to see the high resolution)




>>>>> Segmentation of Adult Brain MR Images (TBA)




>>>>> Medical Image Segmentation with Local Gaussian Distribution (LGD) Fitting Energy [PDF][Matlab Source Code]




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Selected Publication--Journals


  1. Li Wang, Yaozong Gao , Feng Shi , Gang Li , Ken Chung Chen , Zhen Tang , James J Xia , Dinggang Shen, "Automated Segmentation of Dental CBCT Image with Prior-guided Sequential Random Forests", accepted by Medical Physics, 2015.

  2. Li Wang, Yi Ren, Yaozong Gao, Zhen Tang, Ken-Chung Chen, Jiangu Li, Steve GF Shen, Jin Yan, Philip K.M. Lee, Ben Chow, James J. Xia, Dinggang Shen, "Estimating Patient-Specific and Anatomically-Correct Reference Model for Craniomaxillofacial Deformity via Sparse Representation", accepted by Medical Physics, 2015.

  3. Xiaofeng Zhu, Heung-Il Suk, Li Wang, Seong-Whan Lee, Dinggang Shen, "A Novel Relational Regularization Feature Selection Method for Joint Regression and Classification in AD Diagnosis", accepted by Medical Image Analysis, 2015.

  4. Tri Huynh, Yaozong Gao, Jiayin Kang, Li Wang, Pei Zhang, Jun Lian, Dinggang Shen]"Estimating CT Image from MRI Data Using Structured Random Forest and Auto-context Model", IEEE Trans. On Medical Imaging, 2015.

  5. Gang Li, Li Wang, Feng Shi, John H. Gilmore, Weili Lin, Dinggang Shen], Construction of 4D High-definition Cortical Surface Atlases of Infants: Methods and Applications", Medical Image Analysis, 2015.

  6. Feng Shi, Jian Cheng, Li Wang, Pew-Thian Yap, Dinggang Shen, "LRTV: MR Image Super-Resolution with Low-Rank and Total Variation Regularizations", accepted by IEEE Trans. On Medical Imaging, 2015.

  7. Li Wang, Yaozong Gao, Feng Shi, Gang Li, John H. Gilmore, Weili Lin, Dinggang Shen. LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images, Neuroimage, 108, 160-172, 2015. [PDF]

  8. Wenlu Zhang, Rongjian Li, Houtao Deng, Li Wang, Weili Lin, Shuiwang Ji, Dinggang Shen. Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation, accepted by Neuroimage, 2014.

  9. Gang Li, Li Wang, Feng Shi, Amanda E Lyall, Weili Lin, John H Gilmore, Dinggang Shen. Mapping longitudinal development of local cortical gyrification in infants from birth to 2 years of age, The Journal of Neuroscience, 34(12),4228-4238, 2014. (Highlighted in the National Institute of Mental Health (NIMH) strategic plan 2015-2020)

  10. Li Wang, Feng Shi, Yaozong Gao, Gang Li, John H. Gilmore, Weili Lin, Dinggang Shen. Integration of Sparse Multi-modality Representation and Anatomical Constraint for Isointense Infant Brain MR Image Segmentation, Neuroimage, 89, 152-164, 2014.[PDF][Code will be released soon].

  11. Li Wang, Feng Shi, Gang Li, Yaozong Gao, Weili Lin, John H. Gilmore, Dinggang Shen. Segmentation of Neonatal Brain MR Images using Patch-Driven Level Sets, Neuroimage, 84, 141-158, 2014. [PDF][PPT][Matlab Source Code will be available] (Most Downloaded Article).

  12. Li Wang, Ken Chung Chen, Yaozong Gao, Feng Shi, Shu Liao, Gang Li, Steve GF Shen, Jin Yan, Philip KM Lee, Ben Chow, Nancy X Liu, James J Xia, Dinggang Shen. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization, 41 (4), 043503 Medical Physics, 2014.

  13. Gang Li, Li Wang, Feng Shi, Amanda E. Lyall, Mihye Ahn, Ziwen Peng, Hongtu Zhu, Weili Lin, John H. Gilmore, Dinggang Shen. Cortical Thickness and Surface Area in Neonates at High Risk for Schizophrenia, accepted for Brain Structure and Function, 2014.

  14. Feng Shi, Li Wang, Guorong Wu, Gang Li, John H Gilmore, Weili Lin, Dinggang Shen. Neonatal atlas construction using sparse representation, accepted for Human Brain Mapping, 2014.

  15. Amanda E Lyall, Feng Shi, Xiujuan Geng, Sandra Woolson, Gang Li, Li Wang, Robert M Hamer, Dinggang Shen, John H Gilmore, Dynamic development of regional cortical thickness and surface area in early childhood, accepted by Cereb. Cortex, 2014.

  16. Gang Li, Jingxin Nie, Li Wang, Feng Shi, Amanda E. Lyall, Weili Lin, John H. Gilmore, and Dinggang Shen. Mapping Longitudinal Hemispheric Structural Asymmetries of the Human Cerebral Cortex From Birth to 2 Years of Age, 24(5), 1289-1300, Cereb. Cortex, 2014.

  17. Gang Li, Li Wang, Feng Shi, Weili Lin, Dinggang Shen. Simultaneous and Consistent Labeling of Longitudinal Dynamic Developing Cortical Surfaces in Infants, accepted for Medical Image Analysis, 2014.

  18. Li Wang, Feng Shi, Pew-Thian Yap, Weili Lin, John H. Gilmore, Dinggang Shen. Longitudinally guided level sets for consistent tissue segmentation of neonates, Human Brain Mapping, 34(4), 956-972, 2013.[PDF][BibTex][Infant processing software].

  19. Chong-Yaw Wee, Li Wang, Feng Shi, Pew-Thian Yap, Dinggang Shen. Diagnosis of Autism Spectrum Disorders Using Regional and Interregional Morphological Features, Human Brain Mapping, 2013.

  20. Li Wang, Feng Shi, Gang Li, Dinggang Shen. 4D Segmentation of Brain MR Images with Constrained Cortical Thickness Variation, PLOS ONE 8(7): e64207, 2013.

  21. Li Wang, Feng Shi, Pew-Thian Yap, John H. Gilmore, Weili Lin, Dinggang Shen. 4D Multi-Modality Tissue Segmentation of Serial Infant Images, PLOS ONE, 7(9), e44596, 2012. [PDF] [Infant processing software].

  22. Feng Shi, Li Wang, Ziwen Peng, Chong-Yaw Wee, and Dinggang Shen. Altered Modular Organization of Structural Cortical Networks in Children with Autism, PLOS ONE 8(5): e63131, 2013.

  23. Yakang Dai, Yaping Wang, Li Wang, Guorong Wu, Feng Shi, Dinggang Shen. aBEAT: A Toolbox for Consistent Analysis of Longitudinal Adult Brain MRI, PLOS ONE 8(4): e60344, 2013.

  24. Minjeong Kim, Guorong Wu, Wei Li, Li Wang, Young-Don Son, Zang-Hee Cho, Dinggang Shen. Automatic hippocampus segmentation of 7.0 Tesla MR images by combining multiple atlases and auto-context models, Neuroimage, 83, 335-345, 2013.

  25. Yakang Dai, Feng Shi, Li Wang, Guorong Wu, Dinggang Shen. iBEAT: A Toolbox for Infant Brain Magnetic Resonance Image Processing, Neuroinformatics, 11(2), 211-225, 2013.

  26. Gang Li, Jingxin Nie, Li Wang, Feng Shi, Weili Lin, John H. Gilmore, Dinggang Shen. Mapping Region-specific Longitudinal Cortical Surface Expansion from Birth to 2 Years Old, accepted for Cerebral Cortex, 23(11), 2724-2733, 2013.

  27. Feng Shi, Li Wang, Yakang Dai, John H Gilmore, Weili Lin, Dinggang Shen. LABEL: Pediatric Brain Extraction Using Learning-based Meta-algorithm, Neuroimage 62(3): 1975-1986, Sep. 2012.

  28. Jingxin Nie, Gang Li, Li Wang, John H. Gilmore, Weili Lin, Dinggang Shen. Computational Growth Model for Measuring Dynamic Cortical Development in the First Year of Life, Cerebral Cortex, 22(10), 2772-2284, 2012.

  29. Li Wang, Feng Shi, Weili Lin, John H. Gilmore, Dinggang Shen. Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Sets, NeuroImage, 58:805-817, 2011. [PDF][BibTex][Infant processing software].

  30. Lei He, Songfeng Zheng, Li Wang. Integrating local distribution information with level set for boundary extraction, Journal of Visual Communication and Image Representation, 21 (4), 343-354, 2010.

  31. Li Wang, Yunjie Chen, Zhaohua Ding, Deshen Xia. Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy, Journal of Neuroscience Methods, 188(2), 2010, p.316-325.

  32. Li Wang, Lei He, Arabinda Mishra, Chunming Li. Active Contours Driven by Local Gaussian Distribution Fitting Energy. Signal Processing, 89(12), 2009,p. 2435-2447 [PDF][BibTex][Matlab Source Code](Most Cited Signal Processing Articles).

  33. Li Wang, Chunming Li, Quansen Sun, Deshen Xia, Chiu-Yen Kao. Active contours driven by local and global intensity fitting energy with application to brain MR images segmentation, Computerized Medical Imaging and Graphics, 33(7), 520-531, 2009 [PDF][BibTex] Top 25 Hottest Articles (Most Cited Computerized Medical Imaging and Graphics Articles.)

  34. Li Wang, Yunjie Chen, Zhihui Wei, Pheng-ann Heng, Deshen Xia. A Novel Model for Brain MR Images Segmentation In the Presence of Intensity Inhomogeneity. Journal of Computer-Aided Design and Computer Graphics. 21 (11), p.1624-1631, 2009, (in Chinese).

  35. Li Wang, Yunjie Chen, Yang Tang, Zhihui Wei, Pheng-ann Heng, Deshen Xia. A Brain MR Images De-bias Model Based on Genetics Algorithm, Journal of Image and Graphics, 10(11), p.2181-1286, 2008, (in Chinese).


  36. Selected Publication--Conferences



  37. Li Wang, Yaozong Gao, Feng Shi, Gang Li, John H. Gilmore, Weili Lin, Dinggang Shen. LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images. MICCAI Workshop on Medical Computer Vision: Algorithms for Big Data (bigMCV 2014), Boston, USA. (Oral Presentation, 1st winner in MICCAI NeoBrainS12 Challenge) [PDF] [PPT] [Code will be released soon]

  38. Gang Li, Li Wang, Feng Shi, Weili Lin, Dinggang Shen, "Constructing 4D Infant Cortical Surface Atlases Based on Dynamic Developmental Trajectories of the Cortex", Medical Image Computing and Computer-Assisted Intervention-MICCAI 2014, 89-96 (Oral Presentation).

  39. Yaozong Gao, Li Wang, Yeqin Shao, Dinggang Shen, "Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images", MICCAI workshop on Machine Learning in Medical Imaging (MLMI) 2014, Boston, USA (Oral Presentation, Best Paper Award).

  40. Yi Ren, Li Wang, Yaozong Gao, Zhen Tang, Ken Chung Chen, Jiafu Li, Steve GF Shen, Jin Yan, Philip K.M. Lee, Ben Chow, James Xia, Dinggang Shen, "Estimating Anatomically-Correct Reference Model for Craniomaxillofacial Deformity via Sparse Representation", MICCAI 2014, Boston, USA, Sep. 14-18, 2014.

  41. Xuchu Wang, Li Wang, HI Suk, Dinggang Shen, "Online Discriminative Multi-atlas Learning for Isointense Infant Brain Segmentation", MICCAI workshop on Machine Learning in Medical Imaging (MLMI) 2014, Boston, USA, 297-305.

  42. Chunjun Qian, Li Wang, Ambereen Yousuf, Aytekin Oto, Dinggang Shen,"In Vivo MRI Based Prostate Cancer Identification with Random Forests and Auto-context Model" MICCAI workshop on Machine Learning in Medical Imaging (MLMI) 2014, Boston, USA, 314-322 (Oral Presentation).

  43. Qian Wang, Guorong Wu, Li Wang, Pengfei Shi, Weili Lin, Dinggang Shen Sparsity-Learning-Based Longitudinal MR Image Registration for Early Brain Development MICCAI workshop on Machine Learning in Medical Imaging (MLMI) 2014, Boston, USA, 1-8.

  44. Li Wang, Ken Chung Chen, Feng Shi, Shu Liao, Gang Li, Yaozong Gao, Steve GF Shen, Jin Yan, Philip K.M. Lee, Ben Chow, Nancy X. Liu, James J. Xia, Dinggang Shen. Automated segmentation of CBCT image using spiral CT atlases and convex optimization, In: Proceedings of medical image computing and computer aided intervention (MICCAI) 2013, Nagoya, Japan, Sep. 22-26, 2013.

  45. Li Wang, Feng Shi, Gang Li, John H. Gilmore, Weili Lin, Dinggang Shen. Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation, In: Proceedings of medical image computing and computer aided intervention (MICCAI) 2013, Nagoya, Japan, Sep. 22-26, 2013.

  46. Gang Li, Li Wang, Feng Shi, Weili Lin, Dinggang Shen. Multi-Atlas Based Simultaneous Labeling of Longitudinal Dynamic Cortical Surfaces in Infants, In: Proceedings of medical image computing and computer aided intervention (MICCAI) 2013, Nagoya, Japan, Sep. 22-26, 2013 (Oral Presentation).

  47. Feng Shi, Jian Cheng, Li Wang, Pew-Thian Yap, and Dinggang Shen. Low-Rank Total Variation for Image Super-Resolution, In: Proceedings of medical image computing and computer aided intervention (MICCAI) 2013, Nagoya, Japan, Sep. 22-26, 2013.

  48. Li Wang, Feng Shi, Gang Li, Weili Lin, John H. Gilmore, Dinggang Shen. Patch-driven Neonatal Brain MRI Segmentation with Sparse Representation and Level Sets, In: Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI) 2013, San Francisco, California, USA, Apr. 7-11, 2013 (Oral Presentation)[PDF][PPT]

  49. Gang Li, Jingxin Nie, Li Wang, Feng Shi, John H. Gilmore, Weili Lin, Dinggang Shen. Measuring Longitudinally Dynamic Cortex Development in Infants by Reconstruction of Consistent Cortical Surfaces, In: Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI) 2013, San Francisco, California, USA, Apr. 7-11, 2013.

  50. Weili Lin, Li Wang, Gang Li, Feng Shi, Jingxin Nie, Dinggang Shen. Quantitative Assessments of Growth Trajectories of Cortical Thickness During the First 18 Mons of Life, In: Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM)'13, Salt Lake City, Utah, USA, Apr 20-26, 2013.

  51. Weili Lin, Wei Gao, Feng Shi, Li Wang, Gang Li, Jingxin Nie, Hongtu Zhu, Dinggang Shen. Coordinated Anatomical Growth of Motor, Sensory, and Visual Networks in Early Infancy, In: Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM)'13, Salt Lake City, Utah, USA, Apr 20-26, 2013.

  52. Feng Shi, Li Wang, Ziwen Peng, Chong-Yaw Wee, and Dinggang Shen. Revealing Morphological Connectome Alterations in Autistic Brain, In: Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM)'13, Salt Lack City, Utah, USA, Apr. 20-26, 2013 (Oral Presentation).

  53. Hongyu An, Yasheng Chen, Yang Yang, Li Wang, Feng Shi, Dinggang Shen, Lester Kwock, Weili Lin. Profiling regional age dependence of metabolites within human brain during the first year, In: Proceedings of Organization for Human Brain Mapping (OHBM)'12, Beijing, China, Jun. 10-14, 2012.

  54. Jingxin Nie, Gang Li, Li Wang, Feng Shi, Weili Lin, John H. Gilmore, Dinggang Shen. Longitudinal development of cortical thickness correlation network in the first two years of life, In: Proceedings of Organization for Human Brain Mapping (OHBM)'12, Beijing, China, Jun. 10-14, 2012.

  55. Li Wang, Feng Shi, Gang Li, Dinggang Shen. 4D Segmentation of Longitudinal Brain MR Images with Consistent Cortical Thickness Measurement, Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - Second International Workshop in conjunction with MICCAI 2012, Nice, France, Oct. 1, 2012.

  56. Jingxin Nie, Gang Li, Li Wang, John H. Gilmore, Weili Lin, and Dinggang Shen. Computational Growth Model for Cortical Development in the First Year of Life, Image Analysis of Human Brain Development (IAHBD) in conjunction with MICCAI 2011, Toronto, Canada, Sep. 22, 2011.

  57. Minjeong Kim, Guorong Wu, Wei Li, Li Wang, Young-Don Son, Zang-Hee Cho, and Dinggang Shen. Segmenting Hippocampus from 7.0 Tesla MR Images by Combining Multiple Atlases and Auto-Context Models, Second International Workshop on Machine Learning in Medical Imaging (MLMI) in conjunction with MICCAI 2011, Toronto, Canada, Sep. 18, 2011.

  58. Feng Shi, Li Wang, John H. Gilmore, Weili Lin, Dinggang Shen. Learning-based Meta-Algorithm for MRI Brain Extraction, In: Proceedings of medical image computing and computer aided intervention (MICCAI) 2011, Toronto, Canada, Sep. 18-22, 2011.

  59. Li Wang, Feng Shi, John H. Gilmore, Weili Lin, Dinggang Shen. Longitudinal guided level-sets for consistent neonatal image segmentation. In: Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM)'11, Montreal, Quebec, Canada, May 7-13, 2011.

  60. Li Wang, Feng Shi, John H. Gilmore, Weili Lin, Dinggang Shen. Automatic Segmentation of Neonatal Images Using Convex Optimization and Coupled Level Set Method. International Workshop on Medical Imaging and Augmented Reality (MIAR) 2010 in conjunction with MICCAI 2010, LNCS, Volume 6326/2010, 1-10, Beijing, China, Sep. 19-20, 2010 (Oral presentation).

  61. Chunming Li, Chris Gatenby, Li Wang, and John Gore. A Robust Parametric Method for Bias Field Estimation and Segmentation of MR Images. In: Proceedings of IEEE conference on Computer Vision and Pattern Recognition (CVPR), 218-223, 2009.

  62. Li Wang, Jim Macione, Quansen Sun, Deshen Xia, Chunming Li. Level set segmentation based on local gaussian distribution fitting, Asian Conference on Computer Vision (ACCV) 293-302, 2009 (Oral presentation).

  63. Li Wang, Chunming Li, Quansen Sun, Deshen Xia, Chiu-Yen Kao. Brain MR image segmentation using local and global intensity fitting active contours/surfaces. In: Proceedings of medical image computing and computer aided intervention (MICCAI), vol. LNCS 524, 619 Part I. 2008. p.384-392.

Regular reviewer for the following Journals and Conferences:

  • Cerebral Cortex
  • Brain Imaging and Behavior
  • IEEE Transactions on Medical Imaging
  • IEEE Transactions on Image Processing
  • IEEE Transactions on Biomedical Engineering
  • IEEE Transactions on Cybernetics
  • Human Brain Mapping
  • PLOS ONE
  • Pattern Recognition
  • Pattern Recognition Letters
  • NeuroImage
  • Medical Image Analysis
  • Scientific Reports
  • Journal of Biomedical and Health Informatics
  • Neurocomputing
  • Signal Processing
  • British Journal of Mathematics & Computer Science
  • IET Image Processing
  • IET Computer Vision
  • Computer Methods and Programs in Biomedicine
  • International Journal of Computer Assisted Radiology and Surgery
  • Journal of Visual Communication and Image Representation
  • Computational and Mathematical Methods in Medicine
  • Medical & Biological Engineering & Computing
  • International Journal of Pattern Recognition and Artificial Intelligence
  • Signal, Image and Video Processing
  • Current Medical Imaging Reviews
  • Biomedical Engineering: Applications, Basis and Communications (BME)
  • Computers and Mathematics with Applications
  • Medical Image Computing and Computer Aided Intervention (MICCAI), 2011, 2012, 2013, 2014
  • International workshop on Machine Learning in Medical Imaging (MLMI) 2013, 2014
  • 8th International Conference on Image and Graphics (ICIG) 2015

Program Committee for conferences:

  • Chair, MICCAI Grand Challenge on 6-month infant brain MRI segmentation (iSeg-2017), jointly with MICCAI 2017
  • Chair, 7th International Workshop on Machine Learning in Medical Imaging, jointly with MICCAI 2016
  • Co-Chair, 6th International Workshop on Machine Learning in Medical Imaging, jointly with MICCAI 2015
  • 5th International workshop on Machine Learning in Medical Imaging 2014
  • 8th International Conference on Image and Graphics (ICIG) 2015



Education

  • Research Assistant Professor, The University of North Carolina at Chapel Hill, 2017-Present
  • Research Instructor, The University of North Carolina at Chapel Hill, 2015-2016
  • Postdoctoral Research Fellow, The University of North Carolina at Chapel Hill, 2010-2015
  • Ph.D., Pattern Recognition and Intelligent System, Nanjing University of Science and Technology, 2005-2010
  • Thesis in chinese: Research on the segmentation of the brain MR images
  • M.S., Town and Country Planning, Nanjing Agricultural University, 2001-2005