NDLI: Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks

Content Provider	IEEE Xplore Digital Library
Author	Tuan Anh Ngo Carneiro, G.
Copyright Year	2013
Description	Author affiliation: Australian Centre for Visual Technol., Univ. of Adelaide, Adelaide, SA, Australia (Tuan Anh Ngo; Carneiro, G.)
Abstract	This paper introduces a new semi-automated methodology combining a level set method with a top-down segmentation produced by a deep belief network for the problem of left ventricle segmentation from cardiac magnetic resonance images (MRI). Our approach combines the level set advantages that uses several a priori facts about the object to be segmented (e.g., smooth contour, strong edges, etc.) with the knowledge automatically learned from a manually annotated database (e.g., shape and appearance of the object to be segmented). The use of deep belief networks is justified because of its ability to learn robust models with few annotated images and its flexibility that allowed us to adapt it to a top-down segmentation problem. We demonstrate that our method produces competitive results using the database of the MICCAI grand challenge on left ventricle segmentation from cardiac MRI images, where our methodology produces results on par with the best in the field in each one of the measures used in that challenge (perpendicular distance, Dice metric, and percentage of good detections). Therefore, we conclude that our proposed methodology is one of the most competitive approaches in the field.
Starting Page	695
Ending Page	699
File Size	605997
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738143
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Level set Magnetic resonance imaging Training Active contours Databases Shape
Content Type	Text
Resource Type	Article

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