基于模糊标签和深度学习的TOF-MRA影像脑动脉瘤自动检测
收稿日期: 2022-05-24
网络出版日期: 2022-07-20
基金资助
国家自然科学基金资助项目(61672236);上海市科学技术委员会科技创新行动计划临床医学领域项目(19411951200);苏州市科技发展计划项目(SS202072)
Automatic Detection for Cerebral Aneurysms in TOF-MRA Images Based on Fuzzy Label and Deep Learning
Received date: 2022-05-24
Online published: 2022-07-20
脑动脉瘤破裂造成的蛛网膜下腔出血致死致残率极高，借助深度学习网络辅助医生实现高效筛查具有重要意义．为提高基于时间飞跃法磁共振血管造影（Time of Flight-Magnetic Resonance Angiography，TOF-MRA）的脑动脉瘤自动检测的精度，本文基于模糊标签方式，提出一种基于变体3D U-Net和双分支通道注意力（Dual-branch Channel Attention，DCA）的深度神经网络DCAU-Net，DCA模块可以自适应地调整通道特征的响应，提高特征提取能力．首先对260例病例的TOF-MRA影像预处理，将数据集分为174例训练集、43例验证集和43例测试集，然后使用处理后的数据训练和验证DCAU-Net，测试集实验结果表明DCAU-Net可以达到90.69%的敏感度，0.83个/例的假阳性计数和0.52的阳性预测值，有望为动脉瘤筛查提供参考．
陈萌 , 耿辰 , 李郁欣 , 耿道颖 , 鲍奕仿 , 戴亚康 . 基于模糊标签和深度学习的TOF-MRA影像脑动脉瘤自动检测[J]. 波谱学杂志, 2022 , 39(3) : 267 -277 . DOI: 10.11938/cjmr20223004
Subarachnoid hemorrhage caused by the rupture of cerebral aneurysms is extremely fatal and disabling. It’s imperative for radiologists to achieve efficient screening with the help of deep learning-based models. To improve the detection sensitivity of time of flight-magnetic resonance angiography (TOF-MRA) images, this study proposed a neural network named DCAU-Net which is based on fuzzy labels, 3D U-Net variant, and dual-branch channel attention (DCA), and able to adaptively adjust the response of channel features to improve feature extraction capability. First, TOF-MRA images from 260 subjects were preprocessed, and the data were split into the training set (N=174), validation set (N=43) and testing set (N=43). Then the preprocessed data were used for training and validating DCAU-Net. The results show that DCAU-Net scores 90.69% of sensitivity, 0.83 per case of false positive count and 0.52 of positive predicted value in the testing set, providing a promising tool for detecting cerebral aneurysms.
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