NDLI: Auto-encoding of discriminating morphometry from cardiac MRI

Content Provider	IEEE Xplore Digital Library
Author	Dong Hye Ye Desjardins, B. Ferrari, V. Metaxas, D. Pohl, K.M.
Copyright Year	2014
Abstract	We propose a fully-automatic morphometric encoding targeted towards differentiating diseased from healthy cardiac MRI. Existing encodings rely on accurate segmentations of each scan. Segmentation generally includes labour-intensive editing and increases the risk associated with intra- and inter-rater variability. Our morphometric framework only requires the segmentation of a template scan. This template is non-rigidly registered to the other scans. We then confine the resulting deformation maps to the regions outlined by the segmentations. We learn a manifold for each region and identify the most informative coordinates with respect to distinguishing diseased from healthy scans. Compared with volumetric measurements and a deformation-based score, this encoding is much more accurate in capturing morphome-tric patterns distinguishing healthy subjects from those with Tetralogy of Fallot, diastolic dysfunction, and hypertrophic cardiomyopathy.
Starting Page	217
Ending Page	221
File Size	852033
Page Count	5
File Format	PDF
ISBN	9781467319614
DOI	10.1109/ISBI.2014.6867848
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-04-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Encoding Accuracy Image coding Magnetic resonance imaging Diseases Sociology Statistics Disease Classification Morphometry Manifold learning Cardiac MR
Content Type	Text
Resource Type	Article

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