Feature-based, automated segmentation of cerebral infarct patterns using T2- and diffusion-weighted imaging.
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.
Braun
Juergen
J
Department for Medical Informatics, University Hospital Benjamin Franklin, Free University of Berlin, Hindenburgdamm 30, 12200 Berlin, Germany. braun@medizin.fu-berlin.de
Bernarding
Johannes
J
Koennecke
Hans-Christian
HC
Wolf
Karl-Juergen
KJ
Tolxdorff
Thomas
T
D000465:Algorithms; D002544:Cerebral Infarction / Q000145:classification / Q000175:diagnosis*; D016000:Cluster Analysis; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D010363:Pattern Recognition, Automated; D010865:Pilot Projects; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity