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Automatic Construction of Statistical Shape Models for Vertebrae

Becker, Meike and Kirschner, Matthias and Fuhrmann, Simon and Wesarg, Stefan (2011):
Automatic Construction of Statistical Shape Models for Vertebrae.
In: Lecture Notes in Computer Science (LNCS); 6892, Springer, Berlin; Heidelberg; New York, In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011: Part II, pp. 500-507, DOI: 10.1007/978-3-642-23629-7₆₁,
[Conference or Workshop Item]

Abstract

For segmenting complex structures like vertebrae, a priori knowledge by means of statistical shape models (SSMs) is often incorporated. One of the main challenges using SSMs is the solution of the correspondence problem. In this work we present a generic automated approach for solving the correspondence problem for vertebrae. We determine two closed loops on a reference shape and propagate them consistently to the remaining shapes of the training set. Then every shape is cut along these loops and parameterized to a rectangle. There, we optimize a novel combined energy to establish the correspondences and to reduce the unavoidable area and angle distortion. Finally, we present an adaptive resampling method to achieve a good shape representation. A qualitative and quantitative evaluation shows that using our method we can generate SSMs of higher quality than the ICP approach.

Item Type: Conference or Workshop Item
Erschienen: 2011
Creators: Becker, Meike and Kirschner, Matthias and Fuhrmann, Simon and Wesarg, Stefan
Title: Automatic Construction of Statistical Shape Models for Vertebrae
Language: English
Abstract:

For segmenting complex structures like vertebrae, a priori knowledge by means of statistical shape models (SSMs) is often incorporated. One of the main challenges using SSMs is the solution of the correspondence problem. In this work we present a generic automated approach for solving the correspondence problem for vertebrae. We determine two closed loops on a reference shape and propagate them consistently to the remaining shapes of the training set. Then every shape is cut along these loops and parameterized to a rectangle. There, we optimize a novel combined energy to establish the correspondences and to reduce the unavoidable area and angle distortion. Finally, we present an adaptive resampling method to achieve a good shape representation. A qualitative and quantitative evaluation shows that using our method we can generate SSMs of higher quality than the ICP approach.

Series Name: Lecture Notes in Computer Science (LNCS); 6892
Publisher: Springer, Berlin; Heidelberg; New York
Uncontrolled Keywords: Forschungsgruppe Medical Computing (MECO), Statistical shape models (SSM), 3D Model segmentation, Point correspondence, Cutting, Surface parameterization
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011: Part II
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/978-3-642-23629-7₆₁
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