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 代理人
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