Feature-based,Automated Segmentation of Cerebral Infarct Patterns Using T 2- and Diffusion-weighted Imaging |
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Authors: | Juergen Braun Johannes Bernarding Hans-Christian Koennecke Karl-Juergen Wolf Thomas Tolxdorff |
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Institution: | 1. Department for Medical Informatics, University Hospital Benjamin Franklin , Free University of Berlin , Hindenburgdamm 30, Berlin, 12200, Germany;2. Department for Neurology, University Hospital Benjamin Franklin , Free University of Berlin , Hindenburgdamm 30, Berlin, 12200, Germany;3. Department for Radiology, University Hospital Benjamin Franklin , Free University of Berlin , Hindenburgdamm 30, Berlin, 12200, Germany |
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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. |
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Keywords: | Multidimensional Segmentation Automated Histogram Tissue Characterization Diffusion-weighted Imaging |
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