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In recent years, the fully automatic segmentation of the whole heart from three-dimensional (3D) CT or MR images has become feasible with mean surface accuracies in the order of 1mm. The assessment of local myocardial motion and wall thickness for different heart phases requires highly consistent delineation of the involved surfaces. Papillary muscles and misleading pericardial structures lead to challenges that are not easily resolved. This paper presents a framework to train boundary detection functions to explicitly avoid unwanted structures. A two-pass deformable adaptation process allows to reduce false boundary detections in the first pass while detecting most wanted boundaries in a second pass refinement. Cross-validation tests were performed for 67 cardiac datasets from 33 patients. Mean surface accuracies for the left ventricular endo- and epicardium are 0.76mm and 0.68mm, respectively. The percentage of local outliers with segmentation errors &gt; 2mm is reduced by a factor of 3 as compared to a previously published approach. Wall thickness measurements in full 3D demonstrate that artifacts due to irregular endo- and epicardial contours are drastically reduced.

Accurate Segmentation of the Left Ventricle in Computed Tomography Images for Local Wall Thickness Assessment