Cerebrovascular Segmentation in TOF-MRA with Topology Regularization Adversarial Model

Time-of-flight magnetic resonance angiography (TOF-MRA) is a common cerebrovascular imaging. Accurate and automatic cerebrovascular segmentation in TOF-MRA images is an important auxiliary method in clinical practice. Due to the complex semantics and noise interference, the existing segmentation methods often fail to pay attention to topological correlation, resulting in the neglect of branch vessels and vascular topology destruction. In this paper, we proposed a topology regularization adversarial model for cerebrovascular segmentation in TOF-MRA images. Firstly, we trained a self-supervised model to learn spatial semantic layout in TOF-MRA images by image context restoration. Subsequently, we exploited initialization based on the self-supervised model and constructed an adversarial model to accomplish parameter optimization. Considering the limitations of uneven distribution of cerebrovascular classes, we introduced skeleton structures as discriminative features to enhance vessel topological strength. We constructed some latest models to test our method over two datasets. Results show that the proposed model attains the highest score. Therefore, our method can obtain accurate connectivity information and higher graph similarity, leading more meaningful clinical utility.