摘要
Accurate and automatic segmentation of hyper-acute ischemic infarct from magnetic resonance imaging is of great importance in clinical trials. Manual delineation is labor intensive, exhibits great variability due to unclear infarct boundaries, and most importantly, is not practical due to urgent time requirement for prompt therapy. In this paper, segmentation of hyper-acute ischemic infarcts from diffusion weighted imaging based on Support Vector Machine (SVM) is explored. Experiments showed that SVM could provide good agreement with manual delineations by experienced experts to achieve an average Dice coefficient of 0.7630.121. The proposed method could achieve significantly higher segmentation accuracy and could be a potential tool to assist clinicians for quantifying hyper-acute infarction and decision making especially for thrombolytic therapy.
Accurate and automatic segmentation of hyper-acute ischemic infarct from magnetic resonance imaging is of great importance in clinical trials. Manual delineation is labor intensive, exhibits great variability due to unclear infarct boundaries, and most importantly, is not practical due to urgent time requirement for prompt therapy. In this paper, segmentation of hyper-acute ischemic infarcts from diffusion weighted imaging based on Support Vector Machine (SVM) is explored. Experiments showed that SVM could provide good agreement with manual delineations by experienced experts to achieve an average Dice coefficient of 0.7630.121. The proposed method could achieve significantly higher segmentation accuracy and could be a potential tool to assist clinicians for quantifying hyper-acute infarction and decision making especially for thrombolytic therapy.
