Multi-Atlas Based Pathological Stratification of d-TGA Congenital Heart Disease

TL;DR

This paper presents a CAD system that uses features from segmentation errors to classify post-operative d-TGA heart conditions with high accuracy, addressing issues from dissimilar atlas databases in segmentation methods.

Contribution

It introduces a novel feature extraction and decision tree approach for pathological classification in heart MRI, improving diagnosis accuracy.

Findings

Achieved 93.33% overall accuracy in classification.

Validated on 60 heart MRI images with healthy and d-TGA cases.

Demonstrated robustness against atlas dissimilarity issues.

Abstract

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%.