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Deformable Meshes for Medical Image Segmentation

Accurate Automatic Segmentation of Anatomical Structures

  • Book
  • © 2015

Overview

Authors:
  1. Dagmar Kainmueller

    1. MPI für Zellbiologie und Genetik, Dresden, Germany

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  • Publication in the field of technical sciences?
  • Includes supplementary material: sn.pub/extras

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Introduction

    • Dagmar Kainmueller
    Pages 1-13

  3. The Segmentation Framework

    1. Front Matter

      Pages 15-15
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    2. Basic Terms and Notation

      • Dagmar Kainmueller
      Pages 17-23

    3. Deformable Meshes for Automatic Segmentation

      • Dagmar Kainmueller
      Pages 25-49

    4. Omnidirectional Displacements for Deformable Surfaces (ODDS)

      • Dagmar Kainmueller
      Pages 51-66

    5. From Surface Mesh Deformations to Volume Deformations

      • Dagmar Kainmueller
      Pages 67-77

  4. Applications to Medical Image Data

    1. Front Matter

      Pages 79-79
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    2. Fundamentals of Quantitative Evaluation

      • Dagmar Kainmueller
      Pages 81-87

    3. Single-object Segmentation of Anatomical Structures

      • Dagmar Kainmueller
      Pages 89-115

    4. Multi-object Segmentation of Joints

      • Dagmar Kainmueller
      Pages 117-129

    5. ODDS for Segmentation of Highly Curved Structures

      • Dagmar Kainmueller
      Pages 131-145

    6. Extrapolation and Atlas-based Segmentation of Leg Muscles

      • Dagmar Kainmueller
      Pages 147-160

  5. Back Matter

    Pages 161-180
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Keywords

About this book

​ 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.​

Authors and Affiliations

  • MPI für Zellbiologie und Genetik, Dresden, Germany

    Dagmar Kainmueller

About 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.

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