A pipeline using segmentation, atlas registration, radiomics, and geometric features achieves 87.5% CVD classification accuracy on ASOCA, outperforming direct raw-image classification at 67.5%.
In: Medical Image ComputingandComputer-AssistedIntervention–MICCAI2016:19thInternational Conference, Athens, Greece, October 17-21
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Cardiovascular disease classification using radiomics and geometric features from cardiac CT
A pipeline using segmentation, atlas registration, radiomics, and geometric features achieves 87.5% CVD classification accuracy on ASOCA, outperforming direct raw-image classification at 67.5%.