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Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Statistical shape models for 3D medical image segmentation: a review.

Tobias Heimann¹, Hans-Peter Meinzer

¹Division of Medical and Biological Informatics, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. t.heimann@dkfz.de

Medical Image Analysis

|June 16, 2009

Summary

Statistical shape models (SSMs) are essential for medical image segmentation. This review details creating and using 3D SSMs, focusing on landmark-based methods and exploring alternatives for future developments.

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Area of Science:

Medical Imaging
Computer Vision
Biomedical Engineering

Background:

Statistical shape models (SSMs) are established tools for medical image segmentation.
2D SSMs have been used since the 1990s, with 3D models gaining traction recently due to advances in shape correspondence detection.

Purpose of the Study:

To review techniques for creating and utilizing 3D SSMs.
To provide an overview of the state-of-the-art in statistical shape modeling.

Main Methods:

Focus on landmark-based shape representations, including Active Shape Models (ASMs) and Active Appearance Models (AAMs).
Description of alternative statistical shape modeling approaches.
Structured review covering shape representation, model construction, shape correspondence, local appearance models, and search algorithms.

Main Results:

Comprehensive overview of current techniques for 3D SSM creation and application.
Detailed examination of popular landmark-based modeling variants.

Conclusions:

SSMs are crucial for medical image segmentation.
The review highlights current advancements and discusses future directions in 3D SSMs and their medical applications.