Validation of Automated Biventricular Myocardial Segmentation from Coronary Computed Tomographic Angiography for Multimodality Image Fusion

PurposeImage fusion strategies of myocardial perfusion imaging (MPI) and coronary CT angiography (CCTA) have shown increased diagnostic power. However, their clinical feasibility is hindered by the lack of efficient algorithms for the extraction of cardiac anatomy from CCTA datasets. The aim of this work was to validate our previously published algorithm for automated cardiac segmentation of CCTAs in a larger cohort of subjects while testing its application in clinical settings. MethodsThree borders were automatically and manually extracted on sixty-three clinical CCTAs: left and right endocardia (LV, RV) and the biventricular epicardium (EPI). Impact of image resolutions and inter-operator variability on accuracy and robustness of automated processing were evaluated. Automated algorithm accuracy was assessed with the Dice Similarity Coefficient (DSC) and the surface-to-surface distance metric. Relevant quantities were compared for automated versus manual segmentations: LV and RV volumes, myocardial mass and LV myocardial mass. ResultsLower resolution images offered an acceptable trade-off for accuracy and processing time (45 sec). DSC for LV, RV, EPI borders were 0.88, 0.80 and 0.89. Automated versus manual correlation coefficients for LV and RV vol, myo and LV mass were 0.96, 0.73, 0.84 and 0.67 with inter-operator agreement > 0.93 for three variables. Consistent and improved results were evidenced at higher resolutions. ConclusionOur algorithms allowed efficient automated cardiac segmentation from CT imagery with minimal user intervention, clinically acceptable times and accuracy. The reported results show promise for its use in a clinical environment, specifically in the context of image fusion.