Visualization in Medicine and Life Sciences PDF
Preview Visualization in Medicine and Life Sciences
Mathematics and Visualization SeriesEditors GeraldFarin Hans-ChristianHege DavidHoffman ChristopherR.Johnson KonradPolthier MartinRumpf Lars Linsen Hans Hagen Bernd Hamann Editors Visualization in Medicine and Life Sciences With 76 Figures, 47 in Color ABC Lars Linsen Hans Hagen School of Engineering and Science Technische Universität Kaiserslautern Jacobs University Bremen 67653 Kaiserslautern, Germany P.O. Box 750561 E-mail: [email protected] 28725 Bremen, Germany E-mail: [email protected] Bernd Hamann Department of Computer Science University of California One Shields Avenue Davis, CA 95616-8562, U.S.A E-mail: [email protected] LibraryofCongressControlNumber:2007935102 MathematicsSubjectClassification:68-06, 68U05 ISBN-13 978-3-540-72629-6SpringerBerlinHeidelbergNewYork Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,broadcasting, reproductiononmicrofilmorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9, 1965,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsare liableforprosecutionundertheGermanCopyrightLaw. SpringerisapartofSpringerScience+BusinessMedia springer.com (cid:1)c Springer-VerlagBerlinHeidelberg2008 Theuseofgeneraldescriptivenames,registerednames,trademarks,etc.inthispublicationdoesnotimply, evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevantprotectivelaws andregulationsandthereforefreeforgeneraluse. TypesettingbytheauthorsandSPiusingaSpringerLATEXmacropackage Coverdesign:design&productionGmbH,Heidelberg Printedonacid-freepaper SPIN:12066520 46/SPi/3100 543210 Preface Visualizationtechnologyhasbecomeacrucialcomponentofmedicalandbio- medical data processing and analysis. This technology complements tradi- tionalimageprocessingmethodsasitallowsscientistsandpracticingmedical doctors to visually interact with large, high-resolution three-dimensional im- age data. Further, an ever increasing number of new data acquisition meth- ods is being used in medicine and the life sciences, in particular in genomics and proteomics. The book contains papers discussing some of the latest data processing and visualization techniques and systems for effective analysis of diverse, large, complex, and multi-source data. Internationally leading experts in the area of data visualization came to- getherforaworkshopdedicatedtovisualization inmedicineandlifesciences, held on the island of Ru¨gen, Germany, in July 2006. About 40 participants presented state-of-the-art research on this topic. Research and survey papers were solicited and carefully refereed, resulting in this collection. The research topics covered by the papers in this book deal with these themes: • Segmentation and Feature Detection • Surface Extraction • Volume Visualization • Graph and Network Visualization • Visual Data Exploration • Multivariate and Multidimensional Data Visualization • Large Data Visualization The workshop was supported, in part, by the Deutsche Forschungsgemein- schaft (DFG). Bremen, Germany Lars Linsen Kaiserslautern, Germany Hans Hagen Davis, California, U.S.A. Bernd Hamann June 2007 Contents Part I Surface Extraction Methods from Medical Imaging Data Towards Automatic Generation of 3D Models of Biological Objects Based on Serial Sections Vincent Jasper Dercksen, Cornelia Bru¨ß, Detlev Stalling, Sabine Gubatz, Udo Seiffert, and Hans-Christian Hege ...................... 3 A Topological Approach to Quantitation of Rheumatoid Arthritis Hamish Carr, John Ryan, Maria Joyce, Oliver Fitzgerald, Douglas Veale, Robin Gibney, and Patrick Brennan .......................... 27 3D Visualization of Vasculature: An Overview Bernhard Preim and Steffen Oeltze................................. 39 3D Surface Reconstruction from Endoscopic Videos Arie Kaufman and Jianning Wang ................................. 61 Part II Geometry Processing in Medical Applications A Framework for the Visualization of Cross Sectional Data in Biomedical Research Enrico Kienel, Marek Vanˇco, Guido Brunnett, Thomas Kowalski, Roland Clauß, and Wolfgang Knabe ................................ 77 Towards a Virtual Echocardiographic Tutoring System Gerd Reis, Bernd Lapp´e, Sascha K¨ohn, Christopher Weber, Martin Bertram, and Hans Hagen.................................. 99 Supporting Depth and Motion Perception in Medical Volume Data Jennis Meyer-Spradow, Timo Ropinski, and Klaus Hinrichs ...........121 VIII Contents Part III Visualization of Multi-channel Medical Imaging Data Multimodal Image Registration for Efficient Multi-resolution Visualization Joerg Meyer.....................................................137 A User-friendly Tool for Semi-automated Segmentation and Surface Extraction from Color Volume Data Using Geometric Feature-space Operations Tetyana Ivanovska and Lars Linsen ................................153 Part IV Vector and Tensor Visualization in Medical Applications Global Illumination of White Matter Fibers from DT-MRI Data David C. Banks and Carl-Fredrik Westin............................173 Direct Glyph-based Visualization of Diffusion MR Data Using Deformed Spheres Martin Domin, So¨nke Langner, Norbert Hosten, and Lars Linsen ......185 Visual Analysis of Bioelectric Fields Xavier Tricoche, Rob MacLeod, and Chris R. Johnson ................205 MRI-based Visualisation of Orbital Fat Deformation During Eye Motion Charl P. Botha, Thijs de Graaf, Sander Schutte, Ronald Root, Piotr Wielopolski, Frans C.T. van der Helm, Huibert J. Simonsz, and Frits H. Post ....................................................221 Part V Visualizing Molecular Structures Visual Analysis of Biomolecular Surfaces Vijay Natarajan, Patrice Koehl, Yusu Wang, and Bernd Hamann ......237 BioBrowser – Visualization of and Access to Macro-Molecular Structures Lars Offen and Dieter Fellner .....................................257 Visualization of Barrier Tree Sequences Revisited Christian Heine, Gerik Scheuermann, Christoph Flamm, Ivo L. Hofacker, and Peter F. Stadler ..............................275 Contents IX Part VI Visualizing Gene Expression Data Interactive Visualization of Gene Regulatory Networks with Associated Gene Expression Time Series Data Michel A. Westenberg, Sacha A. F. T. van Hijum, Andrzej T. Lulko, Oscar P. Kuipers, and Jos B. T. M. Roerdink .......................293 Segmenting Gene Expression Patterns of Early-stage Drosophila Embryos Min-Yu Huang, Oliver Ru¨bel, Gunther H. Weber, Cris L. Luengo Hendriks, Mark D. Biggin, Hans Hagen, and Bernd Hamann ..........313 Color Plates ...................................................329 Part I Surface Extraction Methods from Medical Imaging Data Towards Automatic Generation of 3D Models of Biological Objects Based on Serial Sections Vincent Jasper Dercksen1, Cornelia Bru¨ß2, Detlev Stalling3, Sabine Gubatz2, Udo Seiffert2, and Hans-Christian Hege1 1 Zuse Institute Berlin, Germany {dercksen,hege}@zib.de 2 Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany {bruess,gubatz,seiffert}@ipk-gatersleben.de 3 Mercury Computer Systems Inc., Berlin, Germany [email protected] Summary. Wepresentasetofcoherentmethodsforthenearlyautomaticcreation of 3D geometric models from large stacks of images of histological sections. Three- dimensional surface models facilitate the visual analysis of 3D anatomy. They also formabasisforstandardizedanatomicalatlasesthatallowresearcherstointegrate, accumulate and associate heterogeneous experimental information, like functional or gene-expression data, with spatial or even spatio-temporal reference. Models are created by performing the following steps: image stitching, slice alignment, elastic registration,imagesegmentationandsurfacereconstruction.Theproposedmethods are to a large extent automatic and robust against inevitably occurring imaging artifacts. The option of interactive control at most stages of the modeling process complements automatic methods. Key words: Geometry reconstruction, surface representations, registration, segmentation, neural nets 1 Introduction Three-dimensional models help scientists to gain a better understanding of complex biomedical objects. Models provide fundamental assistance for the analysisofanatomy,structure,functionanddevelopment.Theycanforexam- plesupportphenotypingstudies[J.T06]toanswerquestionsabouttherelation between genotypeandphenotype.Anothermajoraimistoestablish anatom- ical atlases. Atlases enable researchers to integrate (spatial) data obtained by different experiments on different individuals into one common framework. A multitude of structural and functional properties can then jointly be visual- izedandanalyzed,revealingnewrelationships.4Datlasescanprovideinsight into temporal development and spatio-temporal relationships. Weintendtoconstructhigh-resolution4Datlasesofdevelopingorganisms, thatallowtheintegrationofexperimentaldata,e.g.geneexpressionpatterns. 4 V.J. Dercksen et al. Such atlases can support the investigation of morphogenesis and gene expres- sion during development. Their creation requires population-based averages of 3D anatomical models representing different developmental stages. By in- terpolating these averages the development over time can be visualized. In this work we focus on the mostly automatic creation of individual 3D modelsasanessentialsteptowardsastandardatlas.Individualmodelscanbe createdfromstacksof2Dimagesofhistologicalsectionsorfromtrue3Dimage data.Withhistologicalsectionshigherresolutionsarepossiblethanforexam- plewith3DNuclearMagneticResonance(NMR)imaging.Furthermore,fluo- rescentdyepenetrationproblems,whichcanoccurwhenimaging(thick)plant tissue with for example (3D) Confocal Laser Scanning Microscopy (CLSM), canbeavoided.Duringdataacquisitionoftenmultipleimagespersectionhave to be created to achieve the desired resolution. These sub-images have to be stitched in an initial mosaicing step. The 3D model construction then contin- ues with the alignment of the image stack to restore the 3D coherence. In the following segmentation step, the structures of interest need to be identified, delimited and labeled. The model is completed by creating a polygonal sur- face, marking the object boundary and separating the structures it consists of. This sequence of nontrivial processing steps is here called the geometry reconstruction pipeline (see Fig. 1). Withourflexiblegeneral-purpose3Dvisualizationsystem[SWH05],three highlyresolved3Dmodelsofgrainshaverecentlybeencreatedbasicallyman- ually [S. 07] (see Fig. 2(a)). Experience gained during the highly interactive modelinghoweverclearlyshowedtheneedfortheautomationandfacilitation of the many repetitive, time-consuming, and work-intensive steps. When creating such detailed models, the size of the data sets is a com- plicating factor. Due to the high resolution of the images and the size of the subject, the data sets frequently do not fit into the main memory of common workstations and must therefore be processed out-of-core. Another problem is that due to the cutting and handling, histological sections are susceptible to imaging artifacts, like cracks, contrast differences and pollution. Very ro- bust processing methods that are able to deal with such imperfect data are therefore required. Fig. 1. Fromphysicalgrainsto3Dmodels:processingstepsofthegeometryrecon- struction pipeline.
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