Jue Wu (John)

headshot1 Jue Wu (John)

Research Overview

My research has been mainly focused on pediatric brain image analysis for fetuses, neonates, young children and adolescents. I adapt and apply mathematical modeling and image processing tools to pediatric datasets, which are challenging data due to their low signal to noise ratio, low resolution with regard to child brain and dynamic structure and function during development. I study the structural neurodevelopment of at risk fetuses and neonates with congenital heart defects, which adversely affect brain growth and maturation. I have also been working on effects of socioeconomic status on brains of healthy neonates, young children and adolescents. I’m also involved in non-pediatric projects, such as epilepsy surgery planning, rodent brain, lung image segmentation and hippocampus segmentation.

Major Research Projects

 Consortium on brain MRI of neonates with CHD – An international consortium with more than 10 hospitals around the globe to standardize CHD brain research nomenclature,   sequences and image analysis tools. Collaboration with Dr. Daniel Licht.

– Socioeconomic status (SES) and hippocampus – A cohort study to look at the relation between SES and whole/regional hippocampal volume in typically developing children with age from 4 to 18 years. This project is part of a larger study led by Prof. Martha Farah.

Surgical planning in epilepsy – A clinical study aiming to improve the visualization of implanted electrode, assist better surgical planning and aid in more accurate resection. Collaboration with Prof. Brian Litt and his lab and Dr. Chengyuan Wu at Jefferson University.

– Quantitative CT measurement of air trapping – A large study of 2 lung CT imaging methods to compare their efficacy as biomarker for brochiolitis obliterans syndrome complicating lung transplantation

– Longitudinal CT study on acute lung injury in a rat model using landmark-based deformable registration. Collaboration with Dr. Rahim Rizi.

What’s New?

– Recently we addressed the problem of the lack of neonatal template with cortical parcelation.

– Our research suggests that there may be a gestational signature of poverty.

Education and Post-Graduate Training

2003 – BSc. in Computer Science,  Fudan University

2008 – PhD in Bioengineering, Hong Kong University of Science and Technology

2009 – Postdoc in Radiology at Penn Image Computing and Science Lab, University of Pennsylvania

Select journal and conference publications

  • J. Wu, P. Schwab, A. Vossough, J. Gee, and Daniel Licht, “Brain Templates for Neonates with Congenital Heart Defects: Preliminary Results from an International Consortium,” in Proc. Joint Annual Meeting ISMRM-ESMRMB, Milan, Italy, 2014, p. abstract no. 5019.
    [Bibtex]
    @CONFERENCE{WuISMRM2014,
    author = {Jue Wu and Peter Schwab and Arastoo Vossough and James Gee and Daniel
    Licht},
    title = {{B}rain {T}emplates for {N}eonates with {C}ongenital {H}eart {D}efects:
    {P}reliminary {R}esults from an {I}nternational {C}onsortium},
    booktitle = {{P}roc. {J}oint {A}nnual {M}eeting {ISMRM}-{ESMRMB}, {M}ilan, {I}taly},
    year = {2014},
    pages = {abstract no. 5019},
    owner = {johnwoo},
    timestamp = {2014.02.12}
    }
  • J. Wu, P. Schwab, A. Vossough, J. Gee, and Daniel Licht, “Brain Templates for Neonates With Congenital Heart Defects: Preliminary Results from an International Consortium,” in Proc. 2014 Pediatric Academic Societies, Vancouver, Canada, 2014.
    [Bibtex]
    @CONFERENCE{WuPAS2014b,
    author = {Jue Wu and Peter Schwab and Arastoo Vossough and James Gee and Daniel
    Licht},
    title = {{B}rain {T}emplates for {N}eonates {W}ith {C}ongenital {H}eart {D}efects:
    {P}reliminary {R}esults from an {I}nternational {C}onsortium},
    booktitle = {{P}roc. 2014 {P}ediatric {A}cademic {S}ocieties, {V}ancouver, {C}anada},
    year = {2014},
    owner = {johnwoo},
    timestamp = {2014.02.12}
    }
  • L. M. Betancourt, N. L. Brodsky, J. Wu, B. Avants, M. Farah, and H. Hurt, “Is There an Effect of Socioeconomic Status (SES) on Infant Neural Development at Age 1 Month?,” in Proc. 2014 Pediatric Academic Societies, Vancouver, Canada, 2014.
    [Bibtex]
    @CONFERENCE{BetancourtPAS2014,
    author = {Laura M. Betancourt and Nancy L. Brodsky and Jue Wu and Brian Avants
    and Martha Farah and Hallam Hurt},
    title = {{I}s {T}here an {E}ffect of {S}ocioeconomic {S}tatus ({SES}) on {I}nfant
    {N}eural {D}evelopment at {A}ge 1 {M}onth?},
    booktitle = {{P}roc. 2014 {P}ediatric {A}cademic {S}ocieties, {V}ancouver, {C}anada},
    year = {2014},
    owner = {johnwoo},
    timestamp = {2014.02.12}
    }
  • J. Wu, M. Ashtari, L. M. Betancourt, N. L. Brodsky, J. Giannetta, J. Gee, H. Hurt, and B. Avants, “Cortical Parcellation for Neonatal Brains in MRI,” in Proc. 2014 Pediatric Academic Societies, Vancouver, Canada, 2014.
    [Bibtex]
    @CONFERENCE{WuPAS2014a,
    author = {Jue Wu and Manzar Ashtari and Laura M. Betancourt and Nancy L. Brodsky
    and Joan Giannetta and James Gee and Hallam Hurt and Brian Avants},
    title = {{C}ortical {P}arcellation for {N}eonatal {B}rains in {MRI}},
    booktitle = {{P}roc. 2014 {P}ediatric {A}cademic {S}ocieties, {V}ancouver, {C}anada},
    year = {2014},
    owner = {johnwoo},
    timestamp = {2014.02.12}
    }
  • J. Wu, M. Ashtari, L. M. Betancourt, N. L. Brodsky, J. Giannetta, J. Gee, H. Hurt, and B. Avants, “Cortical Parcellation for Neonatal Brains,” in IEEE 11th International Symposium on Biomedical Imaging, Beijing, China, 2014 (Oral presentation).
    [Bibtex]
    @CONFERENCE{WuISBI2014,
    author = {Jue Wu and Manzar Ashtari and Laura M. Betancourt and Nancy L. Brodsky
    and Joan Giannetta and James Gee and Hallam Hurt and Brian Avants},
    title = {{C}ortical {P}arcellation for {N}eonatal {B}rains},
    booktitle = {{IEEE} 11th {I}nternational {S}ymposium on {B}iomedical {I}maging},
    year = {2014 (Oral presentation)},
    address = {Beijing, China},
    month = {April},
    owner = {johnwoo},
    timestamp = {2014.02.11}
    }
  • [DOI] A. Vossough, C. Limperopoulos, M. E. Putt, A. J. {du Plessis}, P. J. Schwab, J. Wu, J. C. Gee, and D. J. Licht, “Development and validation of a semiquantitative brain maturation score on fetal MR images: initial results.,” Radiology, vol. 268, iss. 1, pp. 200-207, 2013.
    [Bibtex]
    @ARTICLE{Vossough2013R,
    author = {Vossough, Arastoo and Limperopoulos, Catherine and Putt, Mary E.
    and {du Plessis}, Adre J. and Schwab, Peter J. and Wu, Jue and Gee,
    James C. and Licht, Daniel J.},
    title = {{D}evelopment and validation of a semiquantitative brain maturation
    score on fetal {MR} images: initial results.},
    journal = {{R}adiology},
    year = {2013},
    volume = {268},
    pages = {200--207},
    number = {1},
    month = {Jul},
    abstract = {To develop a valid, reliable, and simple-to-use semiquantitative visual
    scale of fetal brain maturation for use in clinical fetal MR imaging
    assessment and interpretation.This is a retrospective assessment
    of data from a previous study that was prospective, institutional
    review board approved, and HIPAA compliant. Forty-eight normal pregnancies
    with a gestational age (GA) of 25 to 35 weeks were studied. A fetal
    total maturation score (fTMS) was developed by utilizing six subscores
    that evaluated cortical sulcation, myelination, and the germinal
    matrix and provided a single combined numerical value to be evaluated
    as a marker of brain maturity. The fTMS was correlated with GA and
    segmented brain volume. A regression model that associated GA based
    on the visual fTMS scoring was determined. The model was validated
    with a leave-one-out cross validation procedure.Mean GA was 29.3
    weeks ± 2.9 (standard deviation) (range, 25.2-35.3 weeks) and mean
    fTMS was 8.6 ± 3.7 (range, 4-16). The intraclass correlation coefficient
    between the three readers (independent and blinded) was 0.948 (P
    < .001). The correlations were as follows: GA and brain volume, r
    = 0.964 (P < .001); fTMS and brain volume, r = 0.970 (P < .001);
    and GA and fTMS, r = 0.975 (P < .001). A regression model to calculate
    GA based on fTMS yielded the following equation: calculated GA (weeks)
    = 22.86 + 0.748 fTMS (P < .001; adjusted R(2) = 0.946). The standard
    error of the model for calculation of fetal GA from the visual fTMS
    scale was ± 4.8 days.If validated further, the fTMS scale might be
    used to assess morphologic brain maturity of fetuses between 25 and
    35 weeks GA on a single-case basis in a clinical setting.},
    doi = {10.1148/radiol.13111715},
    institution = {{D}epartment of {R}adiology, {C}hildren's {H}ospital of {P}hiladelphia,
    324 {S} 34th {S}t, {W}ood 2115, {P}hiladelphia, {PA} 19004, {USA}.
    vossough@e-mail.chop.edu},
    keywords = {Brain Mapping, methods; Brain, embryology; Case-Control Studies; Female;
    Gestational Age; Heart Defects, Congenital; Humans; Longitudinal
    Studies; Magnetic Resonance Imaging, methods; Male; Pregnancy; Regression
    Analysis; Retrospective Studies},
    language = {eng},
    medline-pst = {ppublish},
    owner = {johnwoo},
    pii = {radiol.13111715},
    pmid = {23440324},
    timestamp = {2014.02.11},
    url = {http://dx.doi.org/10.1148/radiol.13111715}
    }
  • G. M. Lawson, J. T. Duda, B. B. Avants, J. Wu, and M. J. Farah, “Associations between children’s socioeconomic status and prefrontal cortical thickness,” Developmental Science, 2013.
    [Bibtex]
    @ARTICLE{Lawson2013,
    author = {Lawson, Gwendolyn M and Duda, Jeffrey T and Avants, Brian B and Wu,
    Jue and Farah, Martha J},
    title = {{A}ssociations between children's socioeconomic status and prefrontal
    cortical thickness},
    journal = {{D}evelopmental {S}cience},
    year = {2013},
    owner = {jtduda},
    publisher = {Wiley Online Library},
    timestamp = {2013.08.09},
    url = {http://onlinelibrary.wiley.com/doi/10.1111/desc.12096/full}
    }
  • G. M. Lawson, J. T. Duda, J. Wu, B. B. Avants, and M. J. Farah, “Association between socioeconomic status and cortical thickness in prefrontal cortical subregions,” in Society for Neuroscience Annual Meeting, New Orleans, LA, 2012.
    [Bibtex]
    @INPROCEEDINGS{Lawson2012,
    author = {G. M. Lawson and J. T. Duda and J. Wu and B. B. Avants and M. J.
    Farah},
    title = {{A}ssociation between socioeconomic status and cortical thickness
    in prefrontal cortical subregions},
    booktitle = {{S}ociety for {N}euroscience {A}nnual {M}eeting, {N}ew {O}rleans,
    {LA}},
    year = {2012},
    month = {Oct},
    confidential = {n},
    owner = {johnwoo},
    timestamp = {2013.05.31}
    }
  • [DOI] C. Li, M. C. Langham, C. L. Epstein, J. F. Magland, J. Wu, J. Gee, and F. W. Wehrli, “Accuracy of the cylinder approximation for susceptometric measurement of intravascular oxygen saturation.,” Magn Reson Med, vol. 67, iss. 3, pp. 808-813, 2012.
    [Bibtex]
    @ARTICLE{Li2012MRM,
    author = {Li, Cheng and Langham, Michael C. and Epstein, Charles L. and Magland,
    Jeremy F. and Wu, Jue and Gee, James and Wehrli, Felix W.},
    title = {{A}ccuracy of the cylinder approximation for susceptometric measurement
    of intravascular oxygen saturation.},
    journal = {{M}agn {R}eson {M}ed},
    year = {2012},
    volume = {67},
    pages = {808--813},
    number = {3},
    month = {Mar},
    abstract = {Susceptometry-based MR oximetry has previously been shown suitable
    for quantifying hemoglobin oxygen saturation in large vessels for
    studying vascular reactivity and quantification of global cerebral
    metabolic rate of oxygen utilization. A key assumption underlying
    this method is that large vessels can be modeled as long paramagnetic
    cylinders. However, bifurcations, tapering, noncircular cross-section,
    and curvature of these vessels produce substantial deviations from
    cylindrical geometry, which may lead to errors in hemoglobin oxygen
    saturation quantification. Here, the accuracy of the "long cylinder"
    approximation is evaluated via numerical computation of the induced
    magnetic field from 3D segmented renditions of three veins of interest
    (superior sagittal sinus, femoral and jugular vein). At a typical
    venous oxygen saturation of 65\%, the absolute error in hemoglobin
    oxygen saturation estimated via a closed-form cylinder approximation
    was 2.6\% hemoglobin oxygen saturation averaged over three locations
    in the three veins studied and did not exceed 5\% for vessel tilt
    angles <30° at any one location. In conclusion, the simulation results
    provide a significant level of confidence for the validity of the
    cylinder approximation underlying MR susceptometry-based oximetry
    of large vessels.},
    doi = {10.1002/mrm.23034},
    institution = {{D}epartment of {R}adiology, {U}niversity of {P}ennsylvania, {P}hiladelphia,
    {P}ennsylvania 19104, {USA}.},
    keywords = {Adult; Algorithms; Femoral Vein; Humans; Image Processing, Computer-Assisted;
    Jugular Veins; Magnetic Resonance Imaging, methods; Male; Oximetry,
    methods; Oxygen, blood; Superior Sagittal Sinus},
    language = {eng},
    medline-pst = {ppublish},
    owner = {johnwoo},
    pmid = {21858859},
    timestamp = {2013.01.31},
    url = {http://dx.doi.org/10.1002/mrm.23034}
    }
  • J. Wu and B. Avants, “Automatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos.” 2012, p. 36.
    [Bibtex]
    @INPROCEEDINGS{Wu2012MGCNBS2N,
    author = {Wu, J. and Avants, B.},
    title = {{A}utomatic {R}egistration-{B}ased {S}egmentation for {N}eonatal
    {B}rains {U}sing {ANT}s and {A}tropos},
    year = {2012},
    pages = {36},
    journal = {{MICCAI} {G}rand {C}hallenge: {N}eonatal {B}rain {S}egmentation 2012
    ({N}eo{B}rain{S}12)},
    owner = {johnwoo},
    timestamp = {2013.01.31}
    }
  • A. Azarion, J. Wu, A. Pearce, J. Wagenaar, K. Davis, Y. Zheng, H. Wang, B. Litt, and J. Gee, “An Open-Source Pipeline for Visualization of Intracranially Implanted Electrodes Using 3D CT-MRI Co-Registration,” in American Epilepsy Society Annual Meeting 2012, Epilepsy Currents online supplement, San Diego, San Diego, USA, 2012, p. Poster No. 1.375.
    [Bibtex]
    @INPROCEEDINGS{Azarion2012,
    author = {A. Azarion and J. Wu and A. Pearce and J. Wagenaar and K. Davis and
    Y. Zheng and H. Wang and B. Litt and J. Gee},
    title = {{A}n {O}pen-{S}ource {P}ipeline for {V}isualization of {I}ntracranially
    {I}mplanted {E}lectrodes {U}sing 3{D} {CT}-{MRI} {C}o-{R}egistration},
    booktitle = {{A}merican {E}pilepsy {S}ociety {A}nnual {M}eeting 2012, {E}pilepsy
    {C}urrents online supplement, {S}an {D}iego},
    year = {2012},
    volume = {1},
    pages = {Poster No. 1.375},
    address = {San Diego, USA},
    owner = {johnwoo},
    timestamp = {2014.02.12}
    }
  • [DOI] B. B. Avants, N. J. Tustison, J. Wu, P. A. Cook, and J. C. Gee, “An open source multivariate framework for n-tissue segmentation with evaluation on public data.,” Neuroinformatics, vol. 9, iss. 4, pp. 381-400, 2011.
    [Bibtex]
    @ARTICLE{Avants2011N,
    author = {Avants, Brian B. and Tustison, Nicholas J. and Wu, Jue and Cook,
    Philip A. and Gee, James C.},
    title = {{A}n open source multivariate framework for n-tissue segmentation
    with evaluation on public data.},
    journal = {{N}euroinformatics},
    year = {2011},
    volume = {9},
    pages = {381--400},
    number = {4},
    month = {Dec},
    abstract = {We introduce Atropos, an ITK-based multivariate n-class open source
    segmentation algorithm distributed with ANTs ( http://www.picsl.upenn.edu/ANTs).
    The Bayesian formulation of the segmentation problem is solved using
    the Expectation Maximization (EM) algorithm with the modeling of
    the class intensities based on either parametric or non-parametric
    finite mixtures. Atropos is capable of incorporating spatial prior
    probability maps (sparse), prior label maps and/or Markov Random
    Field (MRF) modeling. Atropos has also been efficiently implemented
    to handle large quantities of possible labelings (in the experimental
    section, we use up to 69 classes) with a minimal memory footprint.
    This work describes the technical and implementation aspects of Atropos
    and evaluates its performance on two different ground-truth datasets.
    First, we use the BrainWeb dataset from Montreal Neurological Institute
    to evaluate three-tissue segmentation performance via (1) K-means
    segmentation without use of template data; (2) MRF segmentation with
    initialization by prior probability maps derived from a group template;
    (3) Prior-based segmentation with use of spatial prior probability
    maps derived from a group template. We also evaluate Atropos performance
    by using spatial priors to drive a 69-class EM segmentation problem
    derived from the Hammers atlas from University College London. These
    evaluation studies, combined with illustrative examples that exercise
    Atropos options, demonstrate both performance and wide applicability
    of this new platform-independent open source segmentation tool.},
    doi = {10.1007/s12021-011-9109-y},
    institution = {{P}enn {I}mage {C}omputing and {S}cience {L}aboratory, {U}niversity
    of {P}ennsylvania, 3600 {M}arket {S}treet, {S}uite 370, {P}hiladelphia,
    {PA} 19104, {USA}. stnava@gmail.com},
    keywords = {Access to Information; Algorithms; Bayes Theorem; Databases, Factual,
    standards; Humans; Image Processing, Computer-Assisted, methods;
    Internet, standards; Magnetic Resonance Imaging, methods; Models,
    Statistical; Pattern Recognition, Automated, methods; Software, standards},
    language = {eng},
    medline-pst = {ppublish},
    owner = {pcook},
    pmid = {21373993},
    timestamp = {2013.02.19},
    url = {http://dx.doi.org/10.1007/s12021-011-9109-y}
    }
  • Y. Zheng, A. A. Hunter 3rd, J. Wu, H. Wang, J. Gao, M. G. Maguire, and J. C. Gee, “Landmark matching based automatic retinal image registration with linear programming and self-similarities.,” in Inf Process Med Imaging, 2011, pp. 674-685.
    [Bibtex]
    @INPROCEEDINGS{Zheng2011a,
    author = {Zheng, Yuanjie and Hunter, 3rd, Allan A and Wu, Jue and Wang, Hongzhi
    and Gao, Jianbin and Maguire, Maureen G. and Gee, James C.},
    title = {{L}andmark matching based automatic retinal image registration with
    linear programming and self-similarities.},
    booktitle = {{I}nf {P}rocess {M}ed {I}maging},
    year = {2011},
    volume = {22},
    pages = {674--685},
    abstract = {In this paper, we address the problem of landmark matching based retinal
    image registration. Two major contributions render our registration
    algorithm distinguished from many previous methods. One is a novel
    landmark-matching formulation which enables not only a joint estimation
    of the correspondences and transformation model but also the optimization
    with linear programming. The other contribution lies in the introduction
    of a reinforced self-similarities descriptor in characterizing the
    local appearance of landmarks. Theoretical analysis and a series
    of preliminary experimental results show both the effectiveness of
    our optimization scheme and the high differentiating ability of our
    features.},
    keywords = {Algorithms; Humans; Image Enhancement, methods; Image Interpretation,
    Computer-Assisted, methods; Pattern Recognition, Automated, methods;
    Programming, Linear; Reproducibility of Results; Retina, anatomy
    /&/ histology; Retinoscopy, methods; Sensitivity and Specificity;
    Subtraction Technique},
    language = {eng},
    medline-pst = {ppublish},
    owner = {johnwoo},
    pmid = {21761695},
    timestamp = {2013.01.31}
    }
  • [DOI] Y. Zheng, H. Wang, J. Wu, J. Gao, and J. C. Gee, “Multiscale analysis revisited: Detection of drusen and vessel in digital retinal images,” in Proc. IEEE Int Biomedical Imaging: From Nano to Macro Symp, 2011, pp. 689-692.
    [Bibtex]
    @INPROCEEDINGS{Zheng2011,
    author = {Yuanjie Zheng and Hongzhi Wang and Jue Wu and Jianbin Gao and Gee,
    J. C.},
    title = {{M}ultiscale analysis revisited: {D}etection of drusen and vessel
    in digital retinal images},
    booktitle = {{P}roc. {IEEE} {I}nt {B}iomedical {I}maging: {F}rom {N}ano to {M}acro
    {S}ymp},
    year = {2011},
    pages = {689--692},
    doi = {10.1109/ISBI.2011.5872500},
    owner = {johnwoo},
    timestamp = {2013.01.31},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5872500}
    }
  • J. Wu, K. Davis, A. Azarion, Y. Zheng, H. Wang, B. Litt, and J. C. Gee, “Brain Parcellation Aids in Electrode Localization in Epileptic Patients,” in Augmented Environments for Computer-Assisted Interventions, 2011, pp. 130-137.
    [Bibtex]
    @INPROCEEDINGS{Wu2011a,
    author = {Jue Wu and Kathryn Davis and Allan Azarion and Yuanjie Zheng and
    Hongzhi Wang and Brian Litt and James C. Gee},
    title = {{B}rain {P}arcellation {A}ids in {E}lectrode {L}ocalization in {E}pileptic
    {P}atients},
    booktitle = {{A}ugmented {E}nvironments for {C}omputer-{A}ssisted {I}nterventions},
    year = {2011},
    pages = {130-137},
    bibsource = {DBLP, http://dblp.uni-trier.de},
    ee = {http://dx.doi.org/10.1007/978-3-642-32630-1_13},
    owner = {johnwoo},
    timestamp = {2013.01.31}
    }
  • J. Wu, S. Awate, D. Licht, B. Avants, Cedric Clouchoux, A. D. Plessis, J. Gee, and C. Limperopoulos, “Cortical Folding Measurement Is a Potential Indicator for Prenatal Brain Maturity,” in MICCAI workshop Image Analysis of Human Brain Development, Toronto, Canada, 2011.
    [Bibtex]
    @INPROCEEDINGS{Wu2011,
    author = {Jue Wu and Suyash Awate and Daniel Licht and Brian Avants and Cedric
    Clouchoux and Adre Du Plessis and James Gee and Catherine Limperopoulos},
    title = {{C}ortical {F}olding {M}easurement {I}s a {P}otential {I}ndicator
    for {P}renatal {B}rain {M}aturity},
    booktitle = {{MICCAI} workshop {I}mage {A}nalysis of {H}uman {B}rain {D}evelopment},
    year = {2011},
    address = {Toronto, Canada},
    confidential = {n},
    owner = {johnwoo},
    timestamp = {2013.05.31}
    }
  • J. Wu, W. Cai, and A. C. S. Chung, “POSIT: Part-based object segmentation without intensive training,” Pattern Recognition, vol. 43, iss. 3, pp. 676-684, 2010.
    [Bibtex]
    @ARTICLE{Wu2010PR,
    author = {Jue Wu and Wenchao Cai and Albert C. S. Chung},
    title = {{POSIT}: {P}art-based object segmentation without intensive training},
    journal = {{P}attern {R}ecognition},
    year = {2010},
    volume = {43},
    pages = {676-684},
    number = {3},
    bibsource = {DBLP, http://dblp.uni-trier.de},
    ee = {http://dx.doi.org/10.1016/j.patcog.2009.07.013},
    owner = {johnwoo},
    timestamp = {2013.01.31}
    }
  • B. Avants, A. Klein, N. Tustison, J. Wu, and J. C. Gee, “Evaluation of open-access, automated brain extraction methods on multi-site multi-disorder data,” in 16th annual meeting for the Organization of Human Brain Mapping, 2010.
    [Bibtex]
    @INPROCEEDINGS{Avants2010,
    author = {Avants, B. and Klein, A. and Tustison, N. and Wu, J. and Gee, J.C.},
    title = {{E}valuation of open-access, automated brain extraction methods on
    multi-site multi-disorder data},
    booktitle = {16th annual meeting for the {O}rganization of {H}uman {B}rain {M}apping},
    year = {2010},
    owner = {johnwoo},
    timestamp = {2013.01.31},
    url = {http://www.mindboggle.info/posters/HBM2010poster_Atropos.pdf}
    }
  • D. J. Licht, C. Limperopoulos, A. J. Duplessis, J. C. Gee, J. Wu, G. Hedstrom, M. E. Putt, and A. Vossough, “Development of a Semiquantitative Fetal Brain Maturation Score on MRI,” in ANNALS OF NEUROLOGY, 2010, p. S88.
    [Bibtex]
    @CONFERENCE{Licht2010,
    author = {Licht, D. J. and C. Limperopoulos and A. J. Duplessis and J. C. Gee
    and J. Wu and G. Hedstrom and M. E. Putt and A. Vossough},
    title = {{D}evelopment of a {S}emiquantitative {F}etal {B}rain {M}aturation
    {S}core on {MRI}},
    booktitle = {{ANNALS} {OF} {NEUROLOGY}},
    year = {2010},
    volume = {68},
    pages = {S88},
    owner = {johnwoo},
    timestamp = {2014.02.12}
    }
  • C. Limperopoulos, J. Wu, D. Licht, J. C. Gee, S. P. Awate, C. Clouchoux, and A. J. du Plessis, “Quantitative MRI Measurements of Cortical Development in the Fetus,” in The 2010 Pediatric Academic Societies’ Annual Meeting, 2010 (Oral presentation).
    [Bibtex]
    @CONFERENCE{Limperopoulos2010,
    author = {Catherine Limperopoulos and Jue Wu and Daniel Licht and James C Gee
    and Suyash P Awate and Cedric Clouchoux and Adre J du Plessis},
    title = {{Q}uantitative {MRI} {M}easurements of {C}ortical {D}evelopment in
    the {F}etus},
    booktitle = {{T}he 2010 {P}ediatric {A}cademic {S}ocieties' {A}nnual {M}eeting},
    year = {2010 (Oral presentation)},
    confidential = {n},
    owner = {johnwoo},
    timestamp = {2013.05.31}
    }
  • J. Wu, S. P. Awate, D. Licht, C. Limperopoulos, and J. C. Gee, “Cortical Folding Analysis for Normal Fetuses,” in Proceedings International Society Magnetic Resonance Medicine, 2010 (Oral presentation).
    [Bibtex]
    @INPROCEEDINGS{Wu2010,
    author = {Jue Wu and Suyash P Awate and Daniel Licht and Catherine Limperopoulos
    and James C Gee},
    title = {{C}ortical {F}olding {A}nalysis for {N}ormal {F}etuses},
    booktitle = {{P}roceedings {I}nternational {S}ociety {M}agnetic {R}esonance {M}edicine},
    year = {2010 (Oral presentation)},
    note = {Oral presentation},
    confidential = {n},
    owner = {johnwoo},
    timestamp = {2013.05.31}
    }
  • [DOI] J. Wu and A. C. S. Chung, “A novel framework for segmentation of deep brain structures based on Markov dependence tree.,” Neuroimage, vol. 46, iss. 4, pp. 1027-1036, 2009.
    [Bibtex]
    @ARTICLE{Wu2009N,
    author = {Wu, Jue and Chung, Albert C S.},
    title = {{A} novel framework for segmentation of deep brain structures based
    on {M}arkov dependence tree.},
    journal = {{N}euroimage},
    year = {2009},
    volume = {46},
    pages = {1027--1036},
    number = {4},
    month = {Jul},
    abstract = {The aim of this work is to develop a new framework for multi-object
    segmentation of deep brain structures (caudate nucleus, putamen and
    thalamus) in medical brain images. Deep brain segmentation is difficult
    and challenging because the structures of interest are of relatively
    small size and have significant shape variations. The structure boundaries
    may be blurry or even missing, and the surrounding background is
    full of irrelevant edges. To tackle these problems, we propose a
    template-based framework to fuse the information of edge features,
    region statistics and inter-structure constraints for detecting and
    locating all target brain structures such that initialization by
    hand is unnecessary. The multi-object template is organized in the
    form of a hierarchical Markov dependence tree (MDT), and multiple
    objects are efficiently matched to a target image by a top-to-down
    optimization strategy. The final segmentation is obtained through
    refinement by a B-spline based non-rigid registration between the
    exemplar image and the target image. Our approach needs only one
    example as training data. We have validated the proposed method on
    a publicly available T1-weighted magnetic resonance image database
    with expert-segmented brain structures. In the experiments, the proposed
    approach has obtained encouraging results with 0.80 Dice score for
    the caudate nuclei, 0.81 Dice score for the putamina and 0.84 Dice
    score for the thalami on average.},
    doi = {10.1016/j.neuroimage.2009.03.010},
    institution = {{B}ioengineering {P}rogram, {T}he {H}ong {K}ong {U}niversity of {S}cience
    and {T}echnology, {H}ong {K}ong.},
    keywords = {Brain Mapping, methods; Brain, anatomy /&/ histology; Humans; Image
    Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging;
    Markov Chains},
    language = {eng},
    medline-pst = {ppublish},
    owner = {johnwoo},
    pii = {S1053-8119(09)00231-6},
    pmid = {19286460},
    timestamp = {2013.01.31},
    url = {http://dx.doi.org/10.1016/j.neuroimage.2009.03.010}
    }
  • J. Wu and A. C. S. Chung, “Markov dependence tree-based segmentation of deep brain structures.,” in Med Image Comput Comput Assist Interv, 2008 (Oral presentation), pp. 1092-1100.
    [Bibtex]
    @INPROCEEDINGS{Wu2008,
    author = {Wu, Jue and Chung, Albert C S.},
    title = {{M}arkov dependence tree-based segmentation of deep brain structures.},
    booktitle = {{M}ed {I}mage {C}omput {C}omput {A}ssist {I}nterv},
    year = {2008 (Oral presentation)},
    volume = {11},
    number = {2},
    pages = {1092--1100},
    abstract = {We propose a new framework for multi-object segmentation of deep brain
    structures, which have significant shape variations and relatively
    small sizes in medical brain images. In the images, the structure
    boundaries may be blurry or even missing, and the surrounding background
    is a clutter and full of irrelevant edges. We suggest a template-based
    framework, which fuses the information of edge features, region statistics
    and inter-structure constraints to detect and locate all the targeted
    brain structures such that manual initialization is unnecessary.
    The multi-object template is organized in the form of a hierarchical
    Markov dependence tree. It makes the matching of multiple objects
    efficient. Our approach needs only one example as training data and
    alleviates the demand of a large training set. The obtained segmentation
    results on real data are encouraging and the proposed method enjoys
    several important advantages over existing methods.},
    keywords = {Algorithms; Artificial Intelligence; Brain, anatomy /&/ histology;
    Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted,
    methods; Magnetic Resonance Imaging, methods; Markov Chains; Pattern
    Recognition, Automated, methods; Reproducibility of Results; Sensitivity
    and Specificity},
    language = {eng},
    medline-pst = {ppublish},
    owner = {johnwoo},
    pmid = {18982713},
    timestamp = {2013.02.01}
    }
  • [DOI] J. Wu and A. C. S. Chung, “A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model,” IEEE Transactions on Image Processing, vol. 16, iss. 1, pp. 241-252, 2007.
    [Bibtex]
    @ARTICLE{Wu2007IToIP,
    author = {Jue Wu and Chung, A. C. S.},
    title = {{A} {S}egmentation {M}odel {U}sing {C}ompound {Markov} {R}andom {F}ields
    {B}ased on a {B}oundary {M}odel},
    journal = {{IEEE} {T}ransactions on {I}mage {P}rocessing},
    year = {2007},
    volume = {16},
    pages = {241--252},
    number = {1},
    doi = {10.1109/TIP.2006.884933},
    owner = {johnwoo},
    timestamp = {2013.01.31},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4032831}
    }
  • [DOI] J. Wu and A. C. S. Chung, “Markov Random Field Energy Minimization via Iterated Cross Entropy with Partition Strategy,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing ICASSP 2007, 2007.
    [Bibtex]
    @INPROCEEDINGS{Wu2007,
    author = {Jue Wu and Chung, A. C. S.},
    title = {{Markov} {R}andom {F}ield {E}nergy {M}inimization via {I}terated
    {C}ross {E}ntropy with {P}artition {S}trategy},
    booktitle = {{P}roc. {IEEE} {I}nt. {C}onf. {A}coustics, {S}peech and {S}ignal
    {P}rocessing {ICASSP} 2007},
    year = {2007},
    volume = {1},
    doi = {10.1109/ICASSP.2007.366715},
    owner = {johnwoo},
    timestamp = {2013.02.01},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4217115}
    }
  • [DOI] W. Cai, J. Wu, and A. C. S. Chung, “Shape-Based Image Segmentation Using Normalized Cuts,” in Proc. IEEE Int Image Processing Conf, 2006, pp. 1101-1104.
    [Bibtex]
    @INPROCEEDINGS{Cai2006,
    author = {Wenchao Cai and Jue Wu and Chung, A. C. S.},
    title = {{S}hape-{B}ased {I}mage {S}egmentation {U}sing {N}ormalized {C}uts},
    booktitle = {{P}roc. {IEEE} {I}nt {I}mage {P}rocessing {C}onf},
    year = {2006},
    pages = {1101--1104},
    doi = {10.1109/ICIP.2006.312748},
    owner = {johnwoo},
    timestamp = {2013.02.01},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4106726}
    }