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In general, all algorithms yielded high correlation with MBF and CFR based on manually defined blood TACs for at least one chosen implementation.
FP2 is a path fingerprint that indexes linear subgraphs with a maximal length of 7 atoms, FP3 is based on manually defined SMARTS patterns.
Each algorithm overestimated MBF compared to MBF based on manually defined blood pool TACs.
FADS showed high agreement with MBF based on manually defined blood TACs when using five to seven factors (ICC of 0.971 for MBF when using seven factors).
Nevertheless, agreement with MBF based on manually defined blood TACs was high (ICC of 0.886 for MBF, when using seven clusters).
Cluster analysis with six clusters proved to be the best segmentation algorithm for automatic definition of blood pool TACs, resulting in high correlation and agreement of MBF values with those based on manually defined blood pool VOIs.
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Research in the past was based on manually designed screens with minimal software support.
This larger volume introduces dispersion and partial volume effects in CA t), resulting in higher apparent MBF compared to MBF based on a manually defined AA TAC.
Instead of using complex heuristics to define each layer, simple features are defined and machine learning classifiers are trained based on manually labeled examples.
Quantitative analyses are based on manually selected ground truth data.
Consequently, many care-related activities are still conducted based on manually constructed guidelines.
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