Feasibility of multi-atlas cardiac segmentation from thoracic planning CT in a probabilistic framework.

We present a probabilistic approach to segmentation, which provides a simple method to incorporate inter-observer variation, as well as a useful tool for evaluating the accuracy and sources of error in segmentation.
 
 A dataset consisting of 20 planning computed tomography images of Australian breast cancer patients with delineations of 17 structures (including whole heart, 4 chambers, coronary arteries and valves) was manually contoured by 3 independent observers, following a protocol based on a published reference atlas, with verification by a cardiologist. To develop and validate the segmentation framework a leave-one-out cross-validation strategy was implemented. Performance of the automatic segmentations was evaluated relative to inter-observer variability in manually-derived contours; measures of volume and surface accuracy (Dice similarity coefficient (DSC) and mean absolute surface distance (MASD), respectively) were used to compare automatic segmentation to the consensus segmentation from manual contours.
 
 For the whole heart, the resulting segmentation achieved a DSC of 0.944±0.024, with a MASD of 1.726±1.363mm. Quantitative results, together with the analysis of probabilistic labelling, indicate the feasibility of accurate and consistent segmentation of larger structures, whereas this is not the case for many smaller structures, where a major limitation in segmentation accuracy is the inter-observer variability in manual contour...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research