Left ventricular segmentation from MRI datasets with edge modelling conditional random fields

BMC Medical Imaging

Technical Advance

Ben M Herbst¹  Janto F Dreijer²  Johan A du Preez³ 

[1] Department of Applied Mathematics, Stellenbosch University, Stellenbosch, South Africa;Department of Applied Mathematics, Stellenbosch University, Stellenbosch, South Africa;Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, South Africa;Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, South Africa;

关键词: Ground Truth;    Video Sequence;    Papillary Muscle;    Markov Random Fields;    Conditional Random Field;   

DOI  :  10.1186/1471-2342-13-24

 received in 2012-09-26, accepted in 2013-07-25,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundThis paper considers automatic segmentation of the left cardiac ventricle in short axis magnetic resonance images. Various aspects, such as the presence of papillary muscles near the endocardium border, makes simple threshold based segmentation difficult.MethodsThe endo- and epicardium are modelled as two series of radii which are inter-related using features describing shape and motion. Image features are derived from edge information from human annotated images. The features are combined within a discriminatively trained Conditional Random Field (CRF). Loopy belief propagation is used to infer segmentations when an unsegmented video sequence is given. Powell’s method is applied to find CRF parameters by minimizing the difference between ground truth annotations and the inferred contours. We also describe how the endocardium centre points are calculated from a single human-provided centre point in the first frame, through minimization of frame alignment error.ResultsWe present and analyse the results of segmentation. The algorithm exhibits robustness against inclusion of the papillary muscles by integrating shape and motion information. Possible future improvements are identified.ConclusionsThe presented model integrates shape and motion information to segment the inner and outer contours in the presence of papillary muscles. On the Sunnybrook dataset we find an average Dice metric of 0.91±0.02 and 0.93±0.02 for the inner and outer segmentations, respectively. Particularly problematic are patients with hypertrophy where the blood pool disappears from view at end-systole.

【 授权许可】

CC BY   
© Dreijer et al.; licensee BioMed Central Ltd. 2013