Intracranial aneurysm is a common cerebral vascular disease with a relatively high lethiferous and disable rate. An image-based intelligent and accurate diagnosis method of the disease is urgently needed by the clinic in recent years， for which the accurate segmentation of the vessels and aneurysms is very essential. In this work， we present a novel segmentation framework for the multi-structure intracranial vessels with aneurysms. An adaptive image sampling method is designed using the prior gray-level vascular features， and a Dense mechanism-based network is proposed for the vessel segmentation. Time-of-flight magnetic resonance angiography images of 135 patients （age： 54.7±12.7， 75 males） with intracranial aneurysms are collected for training and testing the framework. Compared with the sampling in the original space and image compression （mean DSC： 0.829 and 0.780）， the adaptive sampling can obviously improve the accuracy of the vessel segmentation （mean DSC： 0.858）. The Dense mechanism-based network can achieve better segmentation result while using less calculation space than the traditional models of 3D UNet， SegNet and DeepLabV3+ （mean DSC： 0.854，0.824 and 0.800）. It also shows good robustness for the segmentation of aneurysms with various locations and sizes.