Discriminative context modeling using auxiliary markers for LV landmark detection from a single MR image

Authors:
Xiaoguang Lu;Marie-Pierre Jolly
Affiliations:
Corporate Technology, Siemens Corporation, Princeton, NJ;Corporate Technology, Siemens Corporation, Princeton, NJ
Venue:
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Year:
2012

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Abstract

Cardiac magnetic resonance imaging (MRI) is a key diagnostic tool for non-invasive assessment of the function and structure of the cardiovascular system in clinical practice. Cardiac landmarks provide strong cues to navigate the complex heart anatomy, extract and evaluate morphological and functional features for diagnosis and disease monitoring. A fully automatic method is presented to detect cardiac landmarks from individual images using a learning-based approach to model discriminative context. In addition to the target landmarks, auxiliary markers are taken into consideration to construct context with more discriminative power. The presented approach is evaluated on the STACOM2012 database, containing 100 independent test cases. Automatic landmark detection targets include two mitral valve landmarks in a long axis image, two RV insert landmarks in a short-axis image, and one central axis point in an LV base image.