Cardiac MRI segmentation with conditional random fields

[摘要] ENGLISH ABSTRACT: This dissertation considers automatic segmentation of the left cardiac ventricle in shortaxis magnetic resonance images. The presence of papillary muscles near the endocardiumborder makes simple threshold based segmentation difficult.The endo- and epicardium are modelled as two series of radii which are inter-related usingfeatures describing shape and motion. Image features are derived from edge informationfrom human annotated images. The features are combined within a Conditional RandomField (CRF) – a discriminatively trained probabilistic model. Loopy belief propagationis used to infer segmentations when an unsegmented video sequence is given. Powell'smethod is applied to find CRF parameters by minimising the difference between groundtruth annotations and the inferred contours. We also describe how the endocardium centrepoints are calculated from a single human-provided centre point in the first frame, throughminimisation of frame alignment error.We present and analyse the results of segmentation. The algorithm exhibits robustnessagainst inclusion of the papillary muscles by integrating shape and motion information.Possible future improvements are identified.

[发布机构] Stellenbosch University