Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium segmentation and epicardial fat volume (EFV) estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a random forest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated computer tomography angiography volumes shows a significant improvement on state-of-the-art in terms of EFV estimation [mean absolute EFV difference: 3.8 ml (4.7%), Pearson correlation: 0.99] with run times suitable for large-scale studies (52 s). Further, the results compare favorably with interobserver variability measured on 10 volumes.
The results suggest a high accuracy and precision for manual measurements of the nodules in chest tomosynthesis images. However, small nodules (<5.0 mm) may be difficult to measure at all because of poor visibility.
Avhandlingen baseras på följande delarbeten
I.Mechanism of injury, injury patterns and associated injuries in patients operated for chest wall trauma. Caragounis E-C, Xiao Y och Granhed H.
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