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Updated: Nov 23, 2025
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Identification of invisible ischemic stroke in noncontrast CT based on novel two-stage convolutional neural network
Guoqing Wu1, Xi Chen1, Jixian Lin2
1Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.
Purpose:
Early identification of ischemic stroke lesion regions plays a vital role in its treatments like thrombolytic therapy and patients' recovery. Noncontrast computed tomography (ncCT) is the most widespread imaging modality in emergency departments. Unfortunately, it is extremely hard to distinguish the lesion from healthy tissue during the hyper-acute phase of stroke. In this paper, a two-stage convolutional neural network-based method was proposed to identify the invisible ischemic stroke from ncCT.
Methods:
In order to combine the global and local information of images effectively, a cascaded structure with two coordinated networks was used to detect the suspicious stroke regions on the whole and optimize the detailed localization. In the first stage, an end-to-end U-net with adaptive threshold was proposed to integrate global position, symmetry and gray texture information to detect the suspicious regions. After reducing the interference from most normal regions, a ResNet-based patch classification network was used to eliminate some false positive samples on suspicious regions by mining deeper image features, contributing to a more precise localization of stroke. Finally, a MAP model was used to optimize the result by combining the classification results of each patch with their spatial constraint information.
Results:
Three independent experiments, that is, training and testing on dataset from one hospital, on the combination of two, and on the two respectively, were performed on a total of 277 cases from two hospitals to validate the proposed model, The proposed method achieved identification accuracy of 91.89%, 87.21%, and 85.71% in the three experiments, and the final localization accuracy in terms of precise localization of stroke were 82.35%, 83.02%, and 81.40%, respectively, which indicated the robustness and clinical values of the method.
Conclusions:
There are some deep image feature differences between stroke region and normal region on ncCT images. The proposed two-stage convolutional neural network model can well seize these features and use them to effectively identify and locate stroke.
