Li Tingting, An Xingwei, Di Yang, He Jiaqian, Liu Shuang, Ming Dong
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300110, China.
Tianjin Center for Brain Science, Tianjin 300110, China.
Entropy (Basel). 2022 Aug 1;24(8):1062. doi: 10.3390/e24081062.
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.
脑动脉瘤的分割是一项具有挑战性的任务，因为其成像特征与血管相似，且前景和背景之间存在极大的不平衡。然而，现有的二维分割方法没有充分利用三维信息，并且忽略了全局特征的影响。在本研究中，我们提出了一种脑动脉瘤分割的自动解决方案。所提出的方法以二维U-Net作为主干，并添加一个Transformer模块来捕获远程信息。此外，通过新的熵选择策略，网络更加关注难以区分的血管和动脉瘤，从而减少类别不平衡的影响。为了引入全局特征，将三个连续的切片作为输入，并生成与中央切片对应的分割图。在推理阶段，使用所提出的重组策略生成分割图，并在CADA数据集上验证了所提出的方法。我们获得了0.944的Dice系数（DSC）、0.941的交并比（IOU）分数、0.946的召回率、0.942的F2分数、0.896的平均精度均值（mAP）和3.12毫米的豪斯多夫距离。
