Application of domain-alignment deep learning methods in CT/MRI image segmentation for acute ischemic stroke
Objective To propose a domain alignment method based on plain scan CT to improve the early and quick diagnosis of acute ischemic stroke(AIS).Methods AIS patients admitted to the Department of Neurology and Neurosurgery of Southern Medical University from January 2020 to December 2022 who received non-contrast head CT,MRI/DWI,ADC and T2-Flair sequence scanning within 3 d after admission were retrospectively analyzed,and a paired CT/MRI image dataset consisting of 318 AIS patients was constructed.The teacher-student image features of each pair were normalized respectively.Then,the training set and the verification set were randomly divided in a ratio of 8∶2.A new generative adversarial network was designed to align cross-mode inputs on the feature layer and transfer semantic knowledge from detailed MRI to CT images for AIS segmentation.A new domain alignment(DA)algorithm(Our DA)was developed.Results Compared with nnUNet,the most powerful medical image segmentation model at present,Our DA was significantly better than nnU-Net,and the segmentation accuracy between each layer verification set was improved by about 15%.Conclusion Our DA model constructed in this study is based on the image features of MRI/DWI sequences and migrated to non-contrast head CT,which has high automatic segmentation performance for AIS lesions on non-contrast head CT,and conducive to early automatic identification of AIS lesions.
acute ischemic strokenon-contrast head CTdomain alignmentdeep learningautomatic segmentationnnU-Net
万方数据
维普
