Multi-Atlas Based Pathological Stratification of d-TGA Congenital Heart Disease
This work addresses a specific medical diagnosis problem for patients with d-TGA, but it is incremental as it builds on existing multi-atlas segmentation methods.
The paper tackled the problem of pathological classification in post-operative d-TGA congenital heart disease by exploiting segmentation errors from poor atlas selection to build a CAD system, achieving an overall accuracy of 93.33% on 60 whole heart MR images.
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%.