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Type: Conference paper
Title: Fully automated non-rigid segmentation with distance regularized level set evolution initialized and constrained by deep-structured inference
Author: Ngo, T.
Carneiro, G.
Citation: Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp.3118-3125
Publisher: IEEE
Issue Date: 2014
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781479951178
ISSN: 1063-6919
Conference Name: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014) (23 Jun 2014 - 28 Jun 2014 : Columbus, OH)
Statement of
Tuan Anh Ngo, Gustavo Carneiro
Abstract: We propose a new fully automated non-rigid segmentation approach based on the distance regularized level set method that is initialized and constrained by the results of a structured inference using deep belief networks. This recently proposed level-set formulation achieves reasonably accurate results in several segmentation problems, and has the advantage of eliminating periodic re-initializations during the optimization process, and as a result it avoids numerical errors. Nevertheless, when applied to challenging problems, such as the left ventricle segmentation from short axis cine magnetic ressonance (MR) images, the accuracy obtained by this distance regularized level set is lower than the state of the art. The main reasons behind this lower accuracy are the dependence on good initial guess for the level set optimization and on reliable appearance models. We address these two issues with an innovative structured inference using deep belief networks that produces reliable initial guess and appearance model. The effectiveness of our method is demonstrated on the MICCAI 2009 left ventricle segmentation challenge, where we show that our approach achieves one of the most competitive results (in terms of segmentation accuracy) in the field.
Keywords: Level sets method, non-rigid segmentation, deep learning, deep inference
Rights: © 2014 IEEE
DOI: 10.1109/CVPR.2014.399
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