Integration- and separation-aware adversarial model for cerebrovascular segmentation from TOF-MRA

Purpose: Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) is important but challenging for the simulation and measurement of cerebrovascular diseases. Recently, deep learning has promoted the rapid development of cerebrovascular segmentation. However, model op-timization relies on voxel or regional punishment and lacks global awareness and interpretation from the texture and edge. To overcome the limitations of the existing methods, we propose a new cerebrovascular segmentation method to obtain more refined structures. Methods: In this paper, we propose a new adversarial model that achieves segmentation using segmenta-tion model and filters the results using discriminator. Considering the sample imbalance in cerebrovascu-lar imaging, we separated the TOF-MRA images and utilized high-and low-frequency images to enhance the texture and edge representation. The encoder weight sharing from the segmentation model not only saves the model parameters, but also strengthens the integration and separation correlation. Diversified discrimination enhances the robustness and regularization of the model. Results: The adversarial model was tested using two cerebrovascular datasets. It scored 82.26% and 73.38%, respectively, ranking first on both datasets. The results show that our method not only outper-forms the recent cerebrovascular segmentation model, but also surpasses the common adversarial mod-els. Conclusion: Our adversarial model focuses on improving the extraction ability of the model on texture and edge, thereby achieving awareness of the global cerebrovascular topology. Therefore, we obtained an accurate and robust cerebrovascular segmentation. This framework has potential applications in many imaging fields, particularly in the application of sample imbalance. Our code is available at the website https://github.com/MontaEllis/ISA-model .(c) 2023 Published by Elsevier B.V.

Automated summary

In this study, a new method for cerebrovascular segmentation is proposed, which enhances the extraction ability of the model on texture and edge by using TOF-MRA images. The testing results on two cerebrovascular datasets show that this method outperforms recent segmentation models and common adversarial models.