A dual-branch hybrid network with bilateral-difference awareness for collateral scoring on CT angiography of acute...

Objective.Acute ischemic stroke (AIS) patients with good collaterals tend to have better outcomes after endovascular therapy. Existing collateral scoring methods rely mainly on vessel segmentation and convolutional neural networks (CNNs), often ignoring bilateral brain differences. This study aims to develop an automated collateral scoring model incorporating bilateral-difference awareness to improve prediction accuracy.Approach.In this paper, we propose a new dual-branch hybrid network to achieve vessel-segmentation-free collateral scoring on the CT Angiography (CTA) of 255 AIS patients. Specifically, we first adopt a data preprocessing method based on maximum intensity projection. To capture the differences between the left and right sides of the brain, we propose a novel bilateral-difference awareness module (BDAM). Then we design a hybrid network that consists of a multi-scale module, a CNN branch, a transformer branch and a feature interaction enhancement module in each stage. In addition, to learn more effective features, we propose a novel local enhancement module and a novel global enhancement module (GEM) to strengthen the local features captured by the CNN branch and the global features of the transformer branch, respectively.Main results.Experiments on a private clinical dataset with CTA images of 255 AIS patients show that our proposed method achieves an accuracy of 85.49% and an intraclass correlation coefficient of 0.9284 for 3-point collateral scoring, outperforming 13 state-of-the-art methods. Besides, for the binary classification tasks (good vs. non-good collateral scoring, poor vs. non-poor collateral scoring), our proposed method also achieves the best accuracies (89.02% and 92.94%).Significance.In this paper, we propose a novel dual-branch hybrid network that incorporates distinct local and GEMs, along with a BDAM, to achieve collateral scoring without the need for vessel segmentation. Our experimental evaluation shows that our model achieves state-of-the-art performance, providing valuable support for improving the efficiency of stroke treatment.