Multimodal Brain Image Analysis: Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings PDF
Preview Multimodal Brain Image Analysis: Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings
Li Shen Tianming Liu Pew-Thian Yap Heng Huang Dinggang Shen Carl-Fredrik Westin (Eds.) 9 Multimodal Brain 5 1 8 S Image Analysis C N L Third International Workshop, MBIA 2013 Held in Conjunction with MICCAI 2013 Nagoya, Japan, September 2013, Proceedings 123 Lecture Notes in Computer Science 8159 CommencedPublicationin1973 FoundingandFormerSeriesEditors: GerhardGoos,JurisHartmanis,andJanvanLeeuwen EditorialBoard DavidHutchison LancasterUniversity,UK TakeoKanade CarnegieMellonUniversity,Pittsburgh,PA,USA JosefKittler UniversityofSurrey,Guildford,UK JonM.Kleinberg CornellUniversity,Ithaca,NY,USA AlfredKobsa UniversityofCalifornia,Irvine,CA,USA FriedemannMattern ETHZurich,Switzerland JohnC.Mitchell StanfordUniversity,CA,USA MoniNaor WeizmannInstituteofScience,Rehovot,Israel OscarNierstrasz UniversityofBern,Switzerland C.PanduRangan IndianInstituteofTechnology,Madras,India BernhardSteffen TUDortmundUniversity,Germany MadhuSudan MicrosoftResearch,Cambridge,MA,USA DemetriTerzopoulos UniversityofCalifornia,LosAngeles,CA,USA DougTygar UniversityofCalifornia,Berkeley,CA,USA GerhardWeikum MaxPlanckInstituteforInformatics,Saarbruecken,Germany Li Shen Tianming Liu Pew-ThianYap Heng Huang Dinggang Shen Carl-Fredrik Westin (Eds.) Multimodal Brain Image Analysis Third International Workshop, MBIA 2013 Held in Conjunction with MICCAI 2013 Nagoya, Japan, September 22, 2013 Proceedings 1 3 VolumeEditors LiShen IndianaUniversitySchoolofMedicine,Indianapolis,IN,USA E-mail:[email protected] TianmingLiu UniversityofGeorgia,Athens,GA,USA E-mail:[email protected] Pew-ThianYap TheUniversityofNorthCarolinaatChapelHill,NC,USA E-mail:[email protected] HengHuang TheUniversityofTexasatArlington,TX,USA E-mail:[email protected] DinggangShen TheUniversityofNorthCarolinaatChapelHill,NC,USA E-mail:[email protected] Carl-FredrikWestin HarvardMedicalSchool,Boston,MA,USA E-mail:[email protected] ISSN0302-9743 e-ISSN1611-3349 ISBN978-3-319-02125-6 e-ISBN978-3-319-02126-3 DOI10.1007/978-3-319-02126-3 SpringerChamHeidelbergNewYorkDordrechtLondon LibraryofCongressControlNumber:2013946482 CRSubjectClassification(1998):I.4,I.5,H.3,I.3.5-8,I.2.10,J.3 LNCSSublibrary: SL6–ImageProcessing,ComputerVision,PatternRecognition,andGraphics ©SpringerInternationalPublishingSwitzerland2013 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped.Exemptedfromthislegalreservationarebriefexcerptsinconnection withreviewsorscholarlyanalysisormaterialsuppliedspecificallyforthepurposeofbeingenteredand executedonacomputersystem,forexclusiveusebythepurchaserofthework.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheCopyrightLawofthePublisher’slocation, initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Permissionsforuse maybeobtainedthroughRightsLinkattheCopyrightClearanceCenter.Violationsareliabletoprosecution undertherespectiveCopyrightLaw. 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Typesetting:Camera-readybyauthor,dataconversionbyScientificPublishingServices,Chennai,India Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) Preface The 3rd international workshop on Multimodal Brain Image Analysis (MBIA) was held on September 22, 2013 in conjunction with the 16th international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) at the Toyoda Auditorium Complex, Higashiyama Campus, Nagoya University, Nagoya, Japan. The objective of MBIA 2013 was to move forward the stateofthe artinanalysismethodologies,algorithms,softwaresystems,val- idationapproaches,benchmarkdatasets,neuroscience,andclinicalapplications. Brainimaging techniques, suchas structural MRI, functional MRI, diffusion MRI, perfusion MRI, EEG, MEG, PET, SPECT, and CT, are playing increas- ingly important roles in elucidating structural, functional and molecular prop- erties in normal and diseased brains. It is widely believed that these different imaging modalities provide distinctive yet complementary information that is critical to the understanding of the working dynamics of the brain. These mul- timodal image data, when coupled with genetic, cognitive and other biomarker data, provide exciting opportunities to enhance our mechanistic understanding of brain function and behavior. However, effective processing, fusion, analysis, andvisualizationofimagesfrommultiplesourcesarestillfacingmajorcomputa- tional challengesowing to the variationin imaging resolutions,spatial-temporal dynamics,andthefundamentalbiophysicalmechanismsthatdeterminethechar- acteristics of the images. The MBIA workshop is a forum dedicated to the ex- changeofideas,data,andsoftwareamongresearchers,withthe goaloffostering the development of innovative technologies that will propel hypothesis testing and data-driven discovery in brain science. Thisyearthe workshopreceived35submissions(including 4invitedpapers). Basedonthe scoresandrecommendationsprovidedby the ProgramCommittee (PC), which consistedof27 notable experts in the field, 24 paperswere selected forposterpresentations.Outofthese15wereselectedforpodiumpresentations. We are enormously grateful to the authors for the high-quality submissions, the PC for evaluating the papers and providing valuable suggestions for im- provingthe papers, the keynote speaker Dr. Polina Golland for her outstanding lecture on “Joint modeling of anatomical and functional connectivity for pop- ulation studies”, all the presenters for their excellent presentations, and the MICCAI organizers and MBIA 2013 attendees for their support. July 2013 Li Shen Tianming Liu Pew-Thian Yap Heng Huang Dinggang Shen Carl-Fredrik Westin Organization Program Committee John Ashburner University College London, UK Vince Calhoun University of New Mexico, USA Gary Christensen University of Iowa, USA Moo K. Chung University of Wisconsin-Madison, USA Rachid Deriche INRIA, France James Gee University of Pennsylvania, USA Xiaoping Hu Emory University, USA Xintao Hu Northwestern Polytechnical University, China Tianzi Jiang Chinese Academy of Science, China David N. Kennedy University of Massachusetts Medical School, USA Sungeun Kim Indiana University School of Medicine, USA Xiaofeng Liu GE Global Research, USA Kwangsik Nho Indiana University School of Medicine, USA Daniel Rueckert Imperial College London, UK Feng Shi UNC Chapel Hill, USA Paul M. Thompson UCLA, USA Qian Wang UNC Chapel Hill, USA Yongmei Wang University of Illinois at Urbana-Champaign, USA Yalin Wang Arizona State University, USA Yang Wang Indiana University School of Medicine, USA Simon K. Warfield HarvardMedical School and BostonChildren’s Hospital, USA Chong-Yaw Wee UNC Chapel Hill, USA Guorong Wu UNC Chapel Hill, USA Thomas Yeo Duke-NUS Graduate Medical School, Singa- pore Daoqiang Zhang Nanjing University of Aeronautics and Astronautics, China Gary Zhang University College London, UK Dajiang Zhu University of Georgia, USA Table of Contents Locally Weighted Multi-atlas Construction .......................... 1 Junning Li, Yonggang Shi, Ivo D. Dinov, and Arthur W. Toga Assessing Structural Organizationand Functional Interaction in Gyral, Sulcal and Cortical Networks ...................................... 9 Xiaojin Li, Xintao Hu, Xi Jiang, Lei Guo, Junwei Han, and Tianming Liu Quantification and Analysis of Large Multimodal Clinical Image Studies: Application to Stroke ..................................... 18 Ramesh Sridharan, Adrian V. Dalca, Kaitlin M. Fitzpatrick, Lisa Cloonan, Allison Kanakis, Ona Wu, Karen L. Furie, Jonathan Rosand, Natalia S. Rost, and Polina Golland Modeling 4D Changes in Pathological Anatomy Using Domain Adaptation: Analysis of TBI Imaging Using a Tumor Database ........ 31 Bo Wang, Marcel Prastawa, Avishek Saha, Suyash P. Awate, Andrei Irimia, Micah C. Chambers, Paul M. Vespa, John D. Van Horn, Valerio Pascucci, and Guido Gerig Bi-modal Non-rigid Registration of Brain MRI Data Based on Deconvolution of Joint Statistics ................................... 40 David Pilutti, Maddalena Strumia, and Stathis Hadjidemetriou Atlas Based Intensity Transformation of Brain MR Images ............ 51 Snehashis Roy, Amod Jog, Aaron Carass, and Jerry L. Prince Use of Diffusion Tensor Images in Glioma Growth Modeling for Radiotherapy Target Delineation................................ 63 Florian Dittmann, Bj¨orn Menze, Ender Konukoglu, and Jan Unkelbach Superpixel-Based Segmentation of Glioblastoma Multiforme from Multimodal MR Images........................................... 74 Po Su, Jianhua Yang, Hai Li, Linda Chi, Zhong Xue, and Stephen T. Wong Mapping Dynamic Changes in Ventricular Volume onto Baseline Cortical Surfaces in Normal Aging, MCI, and Alzheimer’s Disease...... 84 Sarah K. Madsen, Boris A. Gutman, Shantanu H. Joshi, Arthur W. Toga, Clifford R. Jack Jr., Michael W. Weiner, and Paul M. Thompson X Table of Contents Unsupervised Fiber Bundles Registration Using Weighted Measures Geometric Demons............................................... 95 Viviana Siless, Sergio Medina, Pierre Fillard, and Bertrand Thirion Classification Forests and Markov Random Field to Segment Chronic Ischemic Infarcts from Multimodal MRI ............................ 107 Jhimli Mitra, Pierrick Bourgeat, Jurgen Fripp, Soumya Ghose, Stephen Rose, Olivier Salvado, Alan Connelly, Bruce Campbell, Susan Palmer, Gagan Sharma, Soren Christensen, and Leeanne Carey Registration of Brain CT Images to an MRI Template for the Purpose of Lesion-Symptom Mapping ...................................... 119 Hugo J. Kuijf, J. Matthijs Biesbroek, Max A. Viergever, Geert Jan Biessels, and Koen L. Vincken A Dynamical Clustering Model of Brain Connectivity Inspired by the N-Body Problem ................................................ 129 Gautam Prasad, Josh Burkart, Shantanu H. Joshi, Talia M. Nir, Arthur W. Toga, and Paul M. Thompson Cortical Surface Analysis of Multi-contrast MR Data to Improve Detection of Cortical Pathology in Multiple Sclerosis ................. 138 Marika Archambault-Wallenburg, Douglas Arnold, Sridar Narayanan, G. Bruce Pike, and D. Louis Collins PARP1 Gene Variation and Microglial Activity on [11C]PBR28 PET in Older Adults at Risk for Alzheimer’s Disease...................... 150 Sungeun Kim, Kwangsik Nho, Shannon L. Risacher, Mark Inlow, Shanker Swaminathan, Karmen K. Yoder, Li Shen, John D. West, Brenna C. McDonald, Eileen F. Tallman, Gary D. Hutchins, James W. Fletcher, Martin R. Farlow, Bernardino Ghetti, and Andrew J. Saykin A Graph-Based Integration of Multimodal Brain Imaging Data for the Detection of Early Mild Cognitive Impairment (E-MCI)............... 159 Dokyoon Kim, Sungeun Kim, Shannon L. Risacher, Li Shen, Marylyn D. Ritchie, Michael W. Weiner, Andrew J. Saykin, and Kwangsik Nho WholeBrainFunctional ConnectivityUsingMulti-scaleSpatio-Spectral Random Effects Model ........................................... 170 Hakmook Kang, Xue Yang, Frederick W. Bryan, Christina M. Tripp, and Bennett A. Landman Table of Contents XI Modeling Cognitive Processes via Multi-stage Consistent Functional Response Detection .............................................. 180 Jinglei Lv, Dajiang Zhu, Xi Jiang, Kaiming Li, Xintao Hu, Junwei Han, Lei Guo, and Tianming Liu Bivariate Genome-Wide Association Study of Genetically Correlated Neuroimaging Phenotypes from DTI and MRI through a Seemingly Unrelated Regression Model....................................... 189 Neda Jahanshad, Priya Bhatt, Derrek P. Hibar, JulioE.Villalon,TaliaM.Nir,ArthurW.Toga,CliffordR.JackJr., Matt A. Bernstein, Michael W. Weiner, Katie L. McMahon, Greig I. de Zubicaray, Nicholas G. Martin, Margaret J. Wright, and Paul M. Thompson Network-Guided Sparse Learning for Predicting Cognitive Outcomes from MRI Measures .............................................. 202 Jingwen Yan, Heng Huang, Shannon L. Risacher, Sungeun Kim, Mark Inlow, Jason H. Moore, Andrew J. Saykin, and Li Shen A Framework to Compare Tractography Algorithms Based on Their Performance in Predicting Functional Networks...................... 211 Fani Deligianni, Chris A. Clark, and Jonathan D. Clayden Multi-modal Surface-Based Alignment of Cortical Areas Using Intra-cortical T1 Contrast......................................... 222 Christine Lucas Tardif, Juliane Dinse, Andreas Sch¨afer, Robert Turner, and Pierre-Louis Bazin A Heat Kernel Based Cortical Thickness Estimation Algorithm ........ 233 Gang Wang, Xiaofeng Zhang, Qingtang Su, Jiannong Chen, Lili Wang, Yunyan Ma, Qiming Liu, Liang Xu, Jie Shi, and Yalin Wang A Family of Fast Spherical Registration Algorithms for Cortical Shapes ......................................................... 246 Boris A. Gutman, Sarah K. Madsen, Arthur W. Toga, and Paul M. Thompson Author Index.................................................. 259 Locally Weighted Multi-atlas Construction Junning Li, Yonggang Shi, Ivo D. Dinov, and Arthur W. Toga(cid:2) Laboratory of Neuro Imaging, Department of Neurology Universityof California, Los Angeles, CA, USA {junningl,yonggang.shi}@gmail.com, [email protected], [email protected] Abstract. In image-based medical research, atlases are widely used in many tasks, for example, spatial normalization and segmentation. If at- lases are regarded asrepresentativepatternsfor apopulation of images, thenmultipleatlasesarerequiredforaheterogeneouspopulation.Incon- ventional atlas construction methods, the “unit” of representative pat- terns is images. Every input image is associated with its most similar atlas. As the number of subjects increases, the heterogeneity increases accordingly, and a big number of atlases may be needed. In this paper, we explore using region-wise, instead of image-wise, patterns to repre- sent a population. Different parts of an input image is fuzzily associ- ated with different atlases according to voxel-level association weights. Inthisway,regionalstructurepatternsfromdifferentatlasescanbecom- bined together. Based on this model, we design a variational framework formulti-atlasconstruction.Intheapplication totwoT1-weighted MRI datasets,themethodshowspromisingperformance,incomparisonwith a conventional unbiased atlas construction method. 1 Introduction Inimage-basedmedicalresearches,atlasesarewidelyusedtorepresentapopula- tionofimages.Theyprovidecommonspacesforspatialnormalization,references for alignment, and propagation sources for segmentation. One of the most widely used methods is registering input images to a pre- selected reference image, and then taking the average of the warped images as the atlas. Because all the images are transformed to be as similar as possible to thereference,thechoiceofthereferencehassignificantimpactsontheresult.To avoidthebiasintroducedbyarbitrarychoice,theaverageimageorthegeometric mean of the input images can be used as the initial reference, as proposed by Joshi et al. (2004) [1] and Park et al. (2005) [2]. Instead of transforming input imagestowardareferenceimage,Seghersetal.(2004)[3]transformedthemwith the morphological mean of their transformations to all the other images. This method requires registration between all input image pairs. In recent years, manifold-guided group registration methods are developed. Relationship between the input images is modeled with a manifold, and the in- put images are transformedgraduallyalong the manifold to a center, instead of (cid:2) ThisworkissupportedbygrantsK01EB013633, R01MH094343, andP41EB015922 from NIH. L.Shenetal.(Eds.):MBIA2013,LNCS8159,pp.1–8,2013. ©SpringerInternationalPublishingSwitzerland2013
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