  One of the main sources of error in multi-atlas segmentation propagation
approaches comes from the use of atlas databases that are morphologically
dissimilar to the target image. In this work, we exploit the segmentation
errors associated with poor atlas selection to build a computer aided diagnosis
(CAD) system for pathological classification in post-operative
dextro-transposition of the great arteries (d-TGA). The proposed approach
extracts a set of features, which describe the quality of a segmentation, and
introduces them into a logical decision tree that provides the final diagnosis.
We have validated our method on a set of 60 whole heart MR images containing
healthy cases and two different forms of post-operative d-TGA. The reported
overall CAD system accuracy was of 93.33%.
