Discriminative dictionary learning for abdominal multi-organ segmentation.
File(s)
Accepted version
Published version
Author(s)
Type
Journal Article
Abstract
An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.
Date Issued
2015-05-05
Date Acceptance
2015-04-17
Citation
Medical Image Analysis, 2015, 23 (1), pp.92-104
ISSN
1361-8423
Publisher
Elsevier
Start Page
92
End Page
104
Journal / Book Title
Medical Image Analysis
Volume
23
Issue
1
Copyright Statement
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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Subjects
Abdominal multi-organ segmentation
Discriminative dictionary learning
Local atlas selection
Patch based