Background and Objective: Accurate segmentation of cerebral aneurysms in computed tomography angiography (CTA) can provide an essential reference for diagnosis and treatment. This study aimed to evaluate a more helpful image segmentation method for cerebral aneurysms.

Methods: Firstly, the original CTA images were filtered by Gaussian and Laplace, and both the processed image and original image constitute multi-modal images as input. Then, through multiple parallel convolution neural networks to multi-modal image segmentation. Eventually, all of the segmentation results were fused by linear regression to extract cerebral aneurysm and adjacent vessels.

Results: The cerebral aneurysm and adjacent vessels were extracted correctly. When the threshold value is about 0.95, the overall performance of the segmentation effect is the best. The dice, accuracy, and recall rate were different in various combinations of the three extraction methods.

Conclusion: Multi-modal convolutional neural network can improve the segmentation accuracy by multi-modal processing of the original brain CTA image.

背景与目的：计算机断层扫描血管造影（CTA）对脑动脉瘤的准确分割可为诊断和治疗提供重要参考。本研究旨在评估一种更有用的脑动脉瘤图像分割方法。

方法：首先对原始CTA图像进行高斯和拉普拉斯滤波，处理后的图像和原始图像构成多模态图像作为输入。然后，通过多个并行卷积神经网络进行多模态图像分割。最终，所有的分割结果通过线性回归融合以提取脑动脉瘤和邻近血管。

结果：正确提取脑动脉瘤及邻近血管。当阈值在0.95左右时，分割效果的整体表现最好。在三种提取方法的各种组合中，骰子、准确率和召回率都不同。

结论：多模态卷积神经网络可以通过对原始脑CTA图像进行多模态处理来提高分割精度。