Integrating partial volume modeling into automatic coronary lumen segmentation from CCTA raises flow-simulation specificity from 0.6 to 0.68 and AUC from 0.76 to 0.8 for detecting lesions with invasive FFR below 0.8.
Segmentation of the heart and great vessels in CT images using a model-based adaptation framework
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Improving CCTA based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation
Integrating partial volume modeling into automatic coronary lumen segmentation from CCTA raises flow-simulation specificity from 0.6 to 0.68 and AUC from 0.76 to 0.8 for detecting lesions with invasive FFR below 0.8.