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Deformable Meshes for Medical Image Segmentation Accurate Automatic Segmentation of Anatomical Structures

:	Dagmar Kainmueller
:	Deformable Meshes for Medical Image Segmentation Accurate Automatic Segmentation of Anatomical Structures
:	Springer Vieweg
:	9783658070151
:	1
:	CHF 41.30
:

:	Anwendungs-Software
:	English

:	184
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatomical structures, together with thorough evaluations of segmentation accuracy on clinical image data. As compared to related work, these fully automatic pipelines allow for highly accurate segmentation of benchmark image data.

Dagmar Kainmueller works as a research scientist at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany, with a focus on bio image analysis.

	Preface by the Series Editor	7
	Foreword	8
	Acknowledgments	9
	Abstract	10
	Contents	11
	Chapter 1 Introduction	15
	1.1 Motivation	15
	1.1.1 Segmentation of Medical Image Data	16
	1.1.2 Automation of the Segmentation Task	18
	1.1.3 Segmentation Accuracy	19
	1.2 Scope of this Thesis	19
	1.2.1 Automated Segmentation with Deformable Models	19
	1.2.2 Selection of Anatomical Structures and Imaging Modalities	23
	1.2.3 Contribution	23
	1.2.4 Topics Not Discussed	25
	1.3 Structure of this Thesis	26
	Part I The Segmentation Framework	28
	Chapter 2 Basic Terms and Notation	29
	2.1 Images, Segmentations, and Surface Meshes	30
	2.1.1 Three-dimensional Medical Images	30
	2.1.2 Segmentations of Three-dimensional Medical Images	30
	2.1.3 Triangle Surface Meshes	31
	2.1.4 From Segmentations to Surface Meshes and Back	33
	2.2 Deformable Surface Meshes	33
	2.2.1 Displacement Fields and Sets of Candidate Displacements	34
	2.2.2 Appearance Cost	34
	Chapter 3 Deformable Meshes for Automatic Segmentation	36
	3.1 Statistical Shape Models (SSMs) for Segmentation	38
	3.1.1 Generation of SSMs	39
	3.1.2 Prerequisites: Shape Correspondences and Alignment	40
	3.1.3 Image Segmentation via SSM Deformation	41
	3.1.4 Initial Shape Detection	43
	3.1.5 Lack of Image Features: SSMs for Extrapolation	44
	3.2 A Simple Heuristic Appearance Model	45
	3.2.1 Appearance Cost Function	45
	3.2.2 Intensity Parameter Estimation	46
	3.3 Local Search for Appearance Match	46
	3.3.1 Unidirectional Displacements	46
	3.3.2 Optimal Displacement Fields	47
	3.3.3 Intensity Profiles	48
	3.4 Shape-constrained Free Mesh Deformations	48
	3.4.1 Free Deformation within a Narrow Band	50
	3.4.2 Free Deformation with Bounded Displacement Differences	51
	3.5 Simultaneous Free Deformations of Multiple Meshes	54
	3.5.1 Multi-object Graph-based Deformation of Coupled Meshes	55
	3.5.2 Coupling Adjacent Surface Meshes	56
	3.6 Conclusion	60
	Chapter 4 Omnidirectional Displacements for Deformable Surfaces (ODDS)	61
	4.1 The Visibility Problem	62
	4.2 ODDS: Free Mesh Deformations with All-around Visibility	63
	4.2.