The segmentation of cerebral aneurysms is a challenging task because of their similar imaging features to blood vessels and the great imbalance between the foreground and background. However, the existing 2D segmentation methods do not make full use of 3D information and ignore the influence of global features. In this study, we propose an automatic solution for the segmentation of cerebral aneurysms. The proposed method relies on the 2D U-Net as the backbone and adds a Transformer block to capture remote information. Additionally, through the new entropy selection strategy, the network pays more attention to the indistinguishable blood vessels and aneurysms, so as to reduce the influence of class imbalance. In order to introduce global features, three continuous patches are taken as inputs, and a segmentation map corresponding to the central patch is generated. In the inference phase, using the proposed recombination strategy, the segmentation map was generated, and we verified the proposed method on the CADA dataset. We achieved a Dice coefficient (DSC) of 0.944, an IOU score of 0.941, recall of 0.946, an F2 score of 0.942, a mAP of 0.896 and a Hausdorff distance of 3.12 mm.
Keywords: 2D CNN; Transformer; cerebral aneurysm; entropy; segmentation.