Early therapy response assessment of lesions
AssigneeSiemens Healthcare GmbH
InventorsDavid Liu, Shaohua Kevin Zhou, Martin Kramer, Michael Sühling, Christian Tietjen, Grzegorz Soza, Andreas Wimmer
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Abstract
For therapy response assessment, texture features are input for machine learning a classifier and for using a machine learnt classifier. Rather than or in addition to using formula-based texture features, data driven texture features are derived from training images. Such data driven texture features are independent analysis features, such as features from independent subspace analysis. The texture features may be used to predict the outcome of therapy based on a few number of or even one scan of the patient.
Claims13
A method for therapy response assessment, the method comprising:
- obtaining a pre-therapy medical image of a patient, the pre-therapy medical image representing at least one lesion of the patient;
- obtaining a post-therapy medical image of the patient, the post-therapy medical image of the patient representing the at least one lesion of the patient;
- convolving, by a processor, the pre-therapy and post therapy medical images with a texture feature learned from training images, the texture feature comprising an independent sub-space analysis feature;
- and
- classifying, by the processor, a therapy response of the lesion with a machine-learnt classifier with a result of the convolving as an input feature to the machine-learnt classifier.
Dependencies are inferred from the claim text.