Computer-aided diagnosis via model-based shape analysis: automated classification of wall motion abnormalities in echocardiograms.

Rationale And Objective: Shape analysis of endocardial contour sequences from echocardiograms can provide classification of wall motion abnormalities (WMA).

Materials And Methods: We previously reported on active appearance motion models (AAMM) for automated detection of endocardial contours in sequences of echocardiograms. The shape analysis of AAMM renders eigenvariations of shape/motion, including typical normal and pathologic endocardial contraction patterns. A set of stress echocardiograms (single-beat four-chamber and two-chamber sequences with expert-verified endocardial contours) of 129 infarct patients was split randomly into training (n = 65) and testing (n = 64) sets. AAMMs were generated from the training set and AAMM shape coefficients (ASCs) were extracted for all sequences and statistically related to regional/global visual wall motion scoring (VWMS) and volumetric parameters.

Results: Linear regression showed clear correlations between ASCs and VWMS. Discriminant analysis showed good prediction by ASCs of both segmental (74% correctness) and global WMA (90% correctness). Volumetric parameters correlated poorly to regional VWMS.

Conclusion: 1) ASCs show promising accuracy for automated WMA classification. 2) VWMS and endocardial border motion are closely related; with accurate automated border detection, automated WMA classification should be feasible. 3) ASC shape analysis allows contour set evaluation by direct comparison to clinical parameters.