Microvascular decompression (MVD) plays a critical role in the treatment of neurovascular compression-related diseases, with its success heavily dependent on the precise preoperative identification of key anatomical structures, especially small-volume and densely distributed tissues like nerves and vessels. To address this challenge, we propose a multi-attention aggregation network (MAA-Net), for the segmentation of MVD-related structures in MRI images. The method is based on the U-Net architecture and incorporates two attention mechanisms. A spatial-channel parallel attention module at the bottleneck jointly models spatial and channel dependencies to better capture complex interwoven anatomical structures, particularly in regions where nerves and cerebral vessels are intertwined. In addition, a lightweight gated attention module is inserted between the encoder and decoder to improve the perception of small-volume nerve targets by promoting feature selectivity and suppressing background noise, especially when processing fine nerve structures. We evaluated the proposed method on a private clinical dataset covering the brainstem, nerves, cerebral vessels, and cerebellum. The results demonstrate that our method achieves superior performance in volume overlap metrics (e.g., Dice score), delivers precise boundary delineation in distance-based metrics (HD95 and ASD), and exhibits strong recognition ability for elongated structures as reflected in the clDice score, confirming its practical value for preoperative MVD localization.