Deformable meshes for medical image segmentation : accurate automatic segmentation of anatomical structures

184 Pages·2015·1.73 MB·English

by  Kainmueller

Aktuelle Forschung Medizintechnik –
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Institut für Biomedizinische Technik, Micro Technology
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Center, Heidelberg Lehrstuhl für Medizintechnik,
Universität Bochum
Prof. Dr. Edmund Koch,
Klinisches Sensoring und Monitoring,
TU Dresden
Dagmar Kainmueller
Deformable Meshes
for Medical Image
Segmentation
Accurate Automatic Segmentation
of Anatomical Structures
Dagmar Kainmueller
Max Planck Institute of Molecular Cell
Biology and Genetics
Dresden, Germany
Dissertation University of Lübeck, 2013
ISBN 978-3-658-07014-4 ISBN 978-3-658-07015-1 (eBook)
DOI 10.1007/978-3-658-07015-1
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ThebookDeformableMeshesforAccurateAutomaticSegmentationofMedicalIm-
ageDatabyDr.DagmarKainmu¨lleristhe9thvolumeofthenewSpringer-Vieweg
series of excellent theses in medical engineering. The thesis of Dr. Kainmu¨ller has
been selected by an editorial board of highly recognized scientists working in that
field. The Springer-Vieweg series aims to establish a forum for Monographs and
ProceedingsonMedicalEngineering. Theseriespublishesworksthatgiveinsights
into the novel developments in that field. Prospective authors may contact the
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Foreword
“ToBe—ornottoBe: Thatisthequestion!”
This famous quote from 450 years old William Shakespeare from “The Tragicall
Historie of Hamlet, Prince of Denmarke” (Act III, Scene I) is still vivid and vital
andhasnotlostanyofitspowerandmagic. Infact,itcanalsobereadasoneofthe
most beautiful and charming descriptions of the art of segmentation: It manifests
two antagonistic states and raises the fundamental question of how to distinguish
these states. The ability to differentiate between good and bad or important and
irrelevantiskeyinallmattersoflife.
The art of segmentation is central for medical imaging, where an exponential
growth of imaging modalities and images can be observed. On the one hand, this
information is very important and supports clinicians in diagnoses and treatment
validation. On the other hand, simply the immense amount of data overwhelms
the abilities of trained experts as well as the capacities of the health systems.
Automatedproceduresmaypresentaremedytothisdilemma.
In this wonderful and enjoyable book, Dagmar Kainmu¨ller addresses automatic
segmentationofmedicalimagesandcontributessignificantlytotheartofsegmen-
tation. Onthebasisofstatisticalshapemodels,Dagmardevelopsnewtoolswhich
overcome many of the drawbacks of current state alternatives. Remarkable and
price-awardedcontributionsofDagmararetheintroductionofODDS(Omnidirec-
tionalDisplacementsforDeformableSurfaces)andmeshcouplingformulti-object
segmentation, to name just two. Most importantly, Dagmar presents solutions to
real-lifeapplicationssuchasliversegmentationandhipandkneesegmentation.
Many more treasures lie in Dagmar’s book and certainly deserve an extended
acknowledgement,butIliketokeepthereadercuriousandconcludewithanother
quoteofWilliamShakespearefrom“KingHenrytheFifth”(ActIII,SceneII):
“Menoffewwordsarethebestmen.”
Prof. Dr. JanModersitzki
InstituteofMathematicsandImageComputing,UniversityofLu¨beck,Germany
FraunhoferMEVISProjectGroupImageRegistration,Lu¨beck
Acknowledgments
I wish to thank Jan Modersitzki for supervising my thesis. Jan, I am extremely
grateful for everything you taught me! I wish to thank Stefan Zachow and Hans
Lamecker: Thank you with all my heart for your help and advice, for your trust
in my work, and for your friendship. I wish to thank Hans-Christian Hege for
his support throughout my thesis. Many thanks to Matthias Bindernagel and
JudithBergerfortheirgreatwork. ManythankstoThomasLangeforanawesome
collaboration. A special thanks to Heiko Ramm: You are the best office-mate in
the world! Tons of thanks to Andrea Kratz, Britta Weber, Conni Auer, Daniel
Baum, and to the Medical Planning group for their help and support and for the
incrediblynicetimeIhadworkingatZIB.
Ithasbeenapleasureworkingwithyou. Thankyou!
Abstract
Segmentation of anatomical structures in medical image data is an essential task
in clinical practice. Clinical applications that call for image segmentation include
virtualsurgeryplanning,therapyplanning,diagnosis,andpatientmonitoring. This
thesis contributes methods for accurate fully automatic segmentation of certain
anatomicalstructuresin3Dmedicalimagedata. ItfollowstheDeformableModel
approachforsegmentation,andmakesuseofStatisticalShapeModels.
Thecoremethodologicalcontributionofthisthesisisanoveldeformationmodel
fortrianglemeshesthatovercomeslimitationsofstate-of-the-artapproaches. This
deformation model allows for accurate segmentation of tip- and ridge-shaped fea-
tures of anatomical structures. Concerning methodology, a second focus of this
workliesonaccuratemulti-objectsegmentation.
As for practical contributions, this thesis proposes application-specific segmen-
tationpipelinesforarangeofanatomicalstructures, togetherwiththorougheval-
uations of segmentation accuracy on clinical image data. These fully automatic
pipelinesallowforhighlyaccuratesegmentationascomparedtorelatedwork. E.g.,
the pipeline proposed for segmentation of the liver in contrast-enhanced CT is, as
at June 2014, the most accurate among all competing fully automatic approaches
onbenchmarkimagedata.