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Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Vertebrae Segmentation in 3D CT Images Based on a Variational Framework

Authors : Kerstin Hammernik, Thomas Ebner, Darko Stern, Martin Urschler, Thomas Pock

Published in: Recent Advances in Computational Methods and Clinical Applications for Spine Imaging

Publisher: Springer International Publishing

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Abstract

Automatic segmentation of 3D vertebrae is a challenging task in medical imaging. In this paper, we introduce a total variation (TV) based framework that incorporates an a priori model, i.e., a vertebral mean shape, image intensity and edge information. The algorithm was evaluated using leave-one-out cross validation on a data set containing ten computed tomography scans and ground truth segmentations provided for the CSI MICCAI 2014 spine and vertebrae segmentation challenge. We achieve promising results in terms of the Dice Similarity Coefficient (DSC) of \(0.93 \pm 0.04\) averaged over the whole data set.

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previous chapter Lumbar and Thoracic Spine Segmentation Using a Statistical Multi-object ShapePose Model
next chapter Interpolation-Based Shape-Constrained Deformable Model Approach for Segmentation of Vertebrae from CT Spine Images
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Metadata
Title
Vertebrae Segmentation in 3D CT Images Based on a Variational Framework
Authors
Kerstin Hammernik
Thomas Ebner
Darko Stern
Martin Urschler
Thomas Pock
Copyright Year
2015
Book
Recent Advances in Computational Methods and Clinical Applications for Spine Imaging

Print ISBN: 978-3-319-14147-3

Electronic ISBN: 978-3-319-14148-0

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-14148-0_20