Infarct core segmentation using U-Net in CT perfusion imaging: a feasibility study.

Abstract

BackgroundThe wide variability in thresholds on computed tomography (CT) perfusion parametric maps has led to controversy in the stroke imaging community about the most accurate measurement of core infarction.PurposeTo investigate the feasibility of using U-Net to perform infarct core segmentation in CT perfusion imaging.Material and MethodsCT perfusion parametric maps were the input of U-Net, while the ground truth segmentation was determined based on diffusion-weighted imaging (DWI). The dataset used in this study was from the ISLES2018 challenge, which contains 63 acute stroke patients receiving CT perfusion imaging and DWI within 8 h of stroke onset. The segmentation accuracy of model outputs was assessed by calculating Dice similarity coefficient (DSC), sensitivity, and intersection over union (IoU).ResultsThe highest DSC was observed in U-Net taking mean transit time (MTT) or time-to-maximum (Tmax) as input. Meanwhile, the highest sensitivity and IoU were observed in U-Net taking Tmax as input. A DSC in the range of 0.2-0.4 was found in U-Net taking Tmax as input when the infarct area contains < 1000 pixels. A DSC of 0.4-0.6 was found in U-Net taking Tmax as input when the infarct area contains 1000-1999 pixels. A DSC value of 0.6-0.8 was found in U-Net taking Tmax as input when the infarct area contains ≥ 2000 pixels.ConclusionOur model achieved good performance for infarct area containing ≥ 2000 pixels, so it may assist in identifying patients who are contraindicated for intravenous thrombolysis.