Segmentation of core infarct in acute ischemic stroke in diffusion weighted imaging using cascaded VB-Net / 中华放射学杂志

ABSTRACT

Objective:To explore the detection and segmentation of ischemic core infarct volume of the acute stroke in diffusion weighted imaging (DWI) images using cascaded VB-Net.Methods:MRI data of 1 500 patients (2 456 lesions) with acute ischemic stroke in Henan Provincial People′s Hospital from December 2016 to December 2018 were retrospectively analyzed. Firstly, manual segmentation of ischemic core was performed on DWI images (b=1 000 s/mm 2), and then all data were divided into training set, validation set and independent test set by 8∶1∶1. Then, the cascaded VB-Net was constructed, and the core infarct was automatically detected and segmented in the test set. Interclass correlation coefficient (ICC) was used to evaluate the consistency of volume size measured by manual segmentation and cascaded VB-Net. The patients were divided into large ischemic core lesion group (ischemic core volume ≥10 ml) and small ischemic core lesion group (ischemic core volume<10 ml), and the Dice coefficient difference between the two groups was compared using Mann-Whitney U test. Results:In independent test set, cascaded model had the detection rate of 94.6% (243/257) with Dice coefficient of 0.76 (0.68, 0.84). The agreement of cacade VB-Net segmented [4.19(1.21,14.13)ml] and manual segmented ischemic core infarct volume [4.08(1.19,17.92)ml] was high (ICC=0.97, P<0.001). There was no significant difference in Dice coefficient between large and small lesion groups [0.76 (0.69, 0.85), 0.76 (0.67, 0.84), Z=-0.44, P=0.657]. Conclusions:The cascaded VB-Net model provided a tool to realize automatic detection, segmentation, and calculation of ischemic core infarct volume. It has good segmentation accuracy and high consistency with manual segmentation, which can provide an auxiliary decision-making tool for the selection of treatment plans.