Deformable Meshes for Medical Image Segmentation: Accurate Automatic Segmentation of Anatomical Structures (Aktuelle Forschung Medizintechnik – Latest Research in Medical Engineering)

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.​ 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. Contents Deformable Meshes For Accurate Automatic Segmentation Omnidirectional Displacements For Deformable Surfaces (odds) Coupled Deformable Surfaces For Multi-object Segmentation From Surface Mesh Deformations To Volume Deformations Segmentation Of Anatomical Structures In Medical Image Data Target Groups Academics And Practitioners In The Fields Of Computer Science, Medical Imaging, And Automatic Segmentation. The Author 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. The Editor The Series Aktuelle Forschung Medizintechnik - Latest Research In Medical Engineering Is Edited By Thorsten M. Buzug. Deformable Meshes For Accurate Automatic Segmentation -- Omnidirectional Displacements For Deformable Surfaces (odds) -- Coupled Deformable Surfaces For Multi-object Segmentation. Dagmar Kainmueller. Ph. D. University Of Lübeck 2013 Includes Bibliographical References. Mode Of Access: World Wide Web. Front Matter....Pages i-xviii Introduction....Pages 1-13 Front Matter....Pages 15-15 Basic Terms and Notation....Pages 17-23 Deformable Meshes for Automatic Segmentation....Pages 25-49 Omnidirectional Displacements for Deformable Surfaces (ODDS)....Pages 51-66 From Surface Mesh Deformations to Volume Deformations....Pages 67-77 Front Matter....Pages 79-79 Fundamentals of Quantitative Evaluation....Pages 81-87 Single-object Segmentation of Anatomical Structures....Pages 89-115 Multi-object Segmentation of Joints....Pages 117-129 ODDS for Segmentation of Highly Curved Structures....Pages 131-145 Extrapolation and Atlas-based Segmentation of Leg Muscles....Pages 147-160 Back Matter....Pages 161-180