基于3D ResNet50改进模型的TOF-MRA脑动脉瘤分类方法
收稿日期: 2024-06-17
网络出版日期: 2024-08-26
基金资助
山东省自然科学基金(ZR2022QF093);苏州市基础研究试点项目(SHC2022012);江苏省重点研发项目(BE2021012)
A Classification Method for Cerebral Aneurysms in TOF-MRA Based on Improved 3D ResNet50 Model
Received date: 2024-06-17
Online published: 2024-08-26
脑动脉瘤的不规则形态，尤其是子瘤的存在，是动脉瘤破裂风险的关键因素．临床上对子瘤的评估主要是通过时间飞跃法磁共振血管造影（Time of Flight-Magnetic Resonance Angiography，TOF-MRA）进行图像重建及基于医生视觉和经验的判断，这限制了诊断的效率和准确性．本文提出了一种基于3D ResNet50改进的并行多尺度注意力融合网络（Parallel Multiscale Attention Fusion Networks，PMAF-Net）的子瘤自动分类方法，PMAF-Net采用多尺度卷积并加权融合通道和空间注意力权重以提高特征提取能力．实验所用TOF-MRA数据291例，其中训练集128例，验证集32例，测试集131例．与其他分类网络比较，PMAF-Net在测试集上表现最好，准确率为83.97%，召回率为84.48%，精确率为80.33%，F1分数为0.823 5，受试者工作特征曲线（ROC）也显示出模型最佳的分类性能（AUC为0.900 8）．实验结果表明该网络能更准确地识别出子瘤型动脉瘤，有望对动脉瘤破裂风险评估和量化提供支持．
关键词： 时间飞跃法磁共振血管造影; 脑动脉瘤子瘤; 多尺度卷积注意力; 自动分类; 深度学习
薛培阳 , 耿辰 , 李郁欣 , 鲍奕仿 , 鲁宇澄 , 戴亚康 . 基于3D ResNet50改进模型的TOF-MRA脑动脉瘤分类方法[J]. 波谱学杂志, 2025 , 42(1) : 56 -66 . DOI: 10.11938/cjmr20243119
The irregular morphology of cerebral aneurysms, especially the presence of a daughter sac, is a crucial risk factor for aneurysm rupture. Clinical assessment of daughter sac relies mainly on image reconstruction by time of flight-magnetic resonance angiography (TOF-MRA) and judgment based on physicians' vision and experience, which limits the efficiency and accuracy of diagnosis. In this paper, we propose an improved parallel multiscale fusion attention network (PMAF-Net) based on 3D ResNet50 for classification. PMAF-Net uses multi-scale convolution and weighted fusion channel and spatial attention weights to enhance the feature extraction capability. The experiment used 291 cases of TOF-MRA data, including 128 cases in the training set, 32 cases in the validation set, and 131 cases in the test set. Compared with other classification networks, PMAF-Net performs best on the test set, with the accuracy of 83.97%, recall of 84.48%, precision of 80.33%, and F1-score of 0.823 5, and the receiver operating characteristic curve (ROC) also reflects the model's optimal classification performance (AUC of 0.900 8). The results show that the network can identify daughter sac type aneurysms more accurately, which is expected to support the assessment and quantification of the risk of aneurysm rupture.
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