Optimal patient classification via statistical decomposition of a 3D left-ventricular atlas
© Medrano-Gracia et al; licensee BioMed Central Ltd. 2013
Published: 30 January 2013
Finite-element shape models of the LV were customised to 600 cardiac MRI volumes with previously standardised and validated software (CIM v. 6.0, Auckland, NZ). The dataset comprised 300 community-based participants without a history of cardiovascular disease, aged 45-84 from 4 ethnic groups from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort (1) and 300 patients with myocardial infarction from the Defibrillators To Reduce Risk By Magnetic Resonance Imaging Evaluation (DETERMINE) cohort (2) made available through the Cardiac Atlas Project (3). Bias due to scan protocol differences between cohorts was corrected (4). Shape classifiers were constructed to optimally detect which cohort each case belonged. A comparison between principal component analysis (PCA) and information preserving component analysis (IPCA) (5) was performed, using shapes at end-diastole (ED), end-systole (ES) and the difference in shape between ED and ES (ED-ES) which included information on regional wall motion. Traditional clinical classifiers of EF, end-diastolic/end-systolic volume (EDV/ESV) and LVM were also included for comparison. Ten-fold cross-validation experiments were performed in which 90% of the cases were used for training and 10% for validation, repeated 10 times with different training/validation cases.
Specificity and sensitivity are shown in brackets (in that order) for the cross-validation experiments.
This work shows the potential of shape based classification in the automated identification and quantification of heart disease.
This work was supported by award no. R01HL087773 from the NHLBI. The content is solely the responsibility of the authors. The NIH (5R01HL091069) and St. Jude Medical provided grant support for the DETERMINE trial. MESA was supported by contracts N01-HC-95159 through N01-HC-95169 from the NHLBI and by grants UL1-RR-024156 and UL1-RR-025005 from NCRR.
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