基于SE-CaraNet的最大强度投影图像上颅内动脉瘤自动检测方法
[Automatic detection method of intracranial aneurysms on maximum intensity projection images based on SE-CaraNet].
作者信息
Bai Peirui, Song Xuefeng, Liu Qingyi, Liu Jiahui, Cheng Jin, Xiu Xiaona, Ren Yande, Wang Chengjian
机构信息
School of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, P. R. China.
Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong 265000, P. R. China.
出版信息
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Apr 25;41(2):228-236. doi: 10.7507/1001-5515.202301008.
Conventional maximum intensity projection (MIP) images tend to ignore some morphological features in the detection of intracranial aneurysms, resulting in missed detection and misdetection. To solve this problem, a new method for intracranial aneurysm detection based on omni-directional MIP image is proposed in this paper. Firstly, the three-dimensional magnetic resonance angiography (MRA) images were projected with the maximum density in all directions to obtain the MIP images. Then, the region of intracranial aneurysm was prepositioned by matching filter. Finally, the Squeeze and Excitation (SE) module was used to improve the CaraNet model. Excitation and the improved model were used to detect the predetermined location in the omni-directional MIP image to determine whether there was intracranial aneurysm. In this paper, 245 cases of images were collected to test the proposed method. The results showed that the accuracy and specificity of the proposed method could reach 93.75% and 93.86%, respectively, significantly improved the detection performance of intracranial aneurysms in MIP images.
传统的最大密度投影（MIP）图像在颅内动脉瘤检测中往往会忽略一些形态特征，导致漏检和误检。为了解决这个问题，本文提出了一种基于全向MIP图像的颅内动脉瘤检测新方法。首先，对三维磁共振血管造影（MRA）图像进行全方向最大密度投影以获得MIP图像。然后，通过匹配滤波器对颅内动脉瘤区域进行预定位。最后，使用挤压与激励（SE）模块改进CaraNet模型。利用改进后的模型在全向MIP图像的预定位置进行检测，以确定是否存在颅内动脉瘤。本文收集了245例图像来测试所提方法。结果表明，所提方法的准确率和特异性分别可达93.75%和93.86%，显著提高了MIP图像中颅内动脉瘤的检测性能。
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