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• W2034177194 abstract "Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9." @default.
• W2034177194 created "2016-06-24" @default.
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• W2034177194 date "2002-01-01" @default.
• W2034177194 modified "2023-10-18" @default.
• W2034177194 title "Feature-based, Automated Segmentation of Cerebral Infarct Patterns Using T 2- and Diffusion-weighted Imaging" @default.
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• W2034177194 doi "https://doi.org/10.1080/1025584021000011082" @default.
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