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3D morphometry and context modeling for quantitative medical image computing

By Paul Suetens

Abstract

Suetens P., ''3D morphometry and context modeling for quantitative medical image computing'', BioImage Informatics - BII 2014, October 8-10, 2014, Leuven, Belgium (invited speaker).A prerequisite for quantitative image computing is the availability of suitable models that incorporate prior knowledge. A powerful strategy is to construct such models from the data itself by learning from a representative training set of image instances. Local photometric properties are popular descriptive features, but in this talk the emphasis will be on morphometric properties (shape, deformation, motion) and context (clinical variables, genetic variants). Several clinical applications of this approach will illustrate the opportunities for population and disease modeling, therapy outcome prediction, evidence-based medicine, and predicting missing or unobserved data.status: publishe

Topics: PSI_MIC
Year: 2014
OAI identifier: oai:lirias.kuleuven.be:123456789/481818
Provided by: Lirias

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