发明名称 |
SYNTHETIC DATA-DRIVEN HEMODYNAMIC DETERMINATION IN MEDICAL IMAGING |
摘要 |
In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided. |
申请公布号 |
US2016148371(A1) |
申请公布日期 |
2016.05.26 |
申请号 |
US201514804609 |
申请日期 |
2015.07.21 |
申请人 |
Siemens Aktiengesellschaft |
发明人 |
Itu Lucian Mihai;Passerini Tiziano;Rapaka Saikiran;Sharma Puneet;Schwemmer Chris;Schoebinger Max;Redel Thomas;Comaniciu Dorin |
分类号 |
G06T7/00;G06K9/62;G06K9/52 |
主分类号 |
G06T7/00 |
代理机构 |
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代理人 |
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主权项 |
1. A method for hemodynamic determination in medical imaging, the method comprising:
acquiring medical scan data representing a vessel structure of a patient; extracting a set of features from the medical scan data; modifying a first of the features of the set, the modifying representing a change to the vessel structure due to therapy; assigning an uncertainty to the first feature of the set; inputting, by a processor, the features to a machine-trained classifier, the features including the first feature after the modifying and with the uncertainty, the machine trained classifier trained only from synthetic data not specific to any patients; and outputting, by the processor with application of the machine-trained classifier, a hemodynamic metric with a confidence interval for different values of the hemodynamic metric. |
地址 |
Munich DE |