Anatomical feature‐guided semi‐supervised recognition of the vertebrobasilar artery for microvascular decompression

Background Microvascular decompression (MVD) surgery is a surgical procedure commonly used in the treatment of trigeminal neuralgia, which is based on the principle of relieving compression of the responsible blood vessels. Accurate segmentation of the vertebrobasilar artery can provide physicians with more intuitive spatial location relationships, effectively improving the success rate of MVD. In recent years, various learning‐based methods have been widely used for artery segmentation. However, accurately segment the vertebrobasilar artery in complex skull base structures with little labeling data remains a challenge. Purpose This study seeks to identify the vessel responsible for MVD using an anatomical feature‐guided semi‐supervised automated identification approach in clinical data. Methods We proposed a multiscale uncertainty cross‐pseudo‐labeling network with a residual adaptive attention module. Predictions at different scales are generated through multiple decoder levels, emphasizing the reliable part of the prediction and ignoring the regions with low confidence, so that the prediction results at different scales are consistent. Meanwhile, we designed a module called Res‐AdaptiveAttention, which enables the utilization of prior knowledge related to the vertebrobasilar artery. Results Experiments indicated that our method could maintain the overall structure of the vertebrobasilar artery with minimal annotation labels, outperforming current semi‐supervised methods across various metrics. In terms of the dataset, this paper divided the data into a training set, validation set, and test set, with the number of cases being 58 for the training set, eight for the validation set, and 13 for the test set. Additionally, through the evaluation of clinical data, it can accurately display the lesion location, providing significant clinical application value. Conclusions Our method can accurately segment the vertebrobasilar artery in clinical data, and provide visual anatomical references for its positional relationship with the trigeminal nerve. This work lays a foundation for MVD preoperative planning, while further research will focus on quantifying the positional relationship between the segmented artery and trigeminal nerve at multiple anatomical locations to enhance clinical applicability.