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Unsupervised Cerebrovascular Segmentation of TOF-MRA Images Based on Deep Neural Network and Hidden Markov Random Field Model
Frontiers in Neuroinformatics ( IF 3.5 ) Pub Date : 2020-01-10 , DOI: 10.3389/fninf.2019.00077
Shengyu Fan 1, 2, 3 , Yueyan Bian 2 , Hao Chen 4 , Yan Kang 1, 2, 3 , Qi Yang 5 , Tao Tan 6
Affiliation  

Automated cerebrovascular segmentation of time-of-flight magnetic resonance angiography (TOF-MRA) images is an important technique, which can be used to diagnose abnormalities in the cerebrovascular system, such as vascular stenosis and malformation. Automated cerebrovascular segmentation can direct show the shape, direction and distribution of blood vessels. Although deep neural network (DNN)-based cerebrovascular segmentation methods have shown to yield outstanding performance, they are limited by their dependence on huge training dataset. In this paper, we propose an unsupervised cerebrovascular segmentation method of TOF-MRA images based on DNN and hidden Markov random field (HMRF) model. Our DNN-based cerebrovascular segmentation model is trained by the labeling of HMRF rather than manual annotations. The proposed method was trained and tested using 100 TOF-MRA images. The results were evaluated using the dice similarity coefficient (DSC), which reached a value of 0.79. The trained model achieved better performance than that of the traditional HMRF-based cerebrovascular segmentation method in binary pixel-classification. This paper combines the advantages of both DNN and HMRF to train the model with a not so large amount of the annotations in deep learning, which leads to a more effective cerebrovascular segmentation method.

中文翻译:

基于深度神经网络和隐马尔可夫随机场模型的TOF-MRA图像无监督脑血管分割

飞行时间磁共振血管造影(TOF-MRA)图像的自动脑血管分割是一项重要的技术,可用于诊断脑血管系统的异常,如血管狭窄和畸形。自动脑血管分割可以直接显示血管的形状、方向和分布。尽管基于深度神经网络 (DNN) 的脑血管分割方法已显示出出色的性能,但它们受到对庞大训练数据集的依赖的限制。在本文中,我们提出了一种基于 DNN 和隐马尔可夫随机场 (HMRF) 模型的 TOF-MRA 图像无监督脑血管分割方法。我们基于 DNN 的脑血管分割模型是通过 HMRF 的标记而不是手动注释来训练的。使用 100 张 TOF-MRA 图像对所提出的方法进行了训练和测试。使用骰子相似系数 (DSC) 评估结果,该系数达到 0.79。训练后的模型在二元像素分类中取得了比传统的基于 HMRF 的脑血管分割方法更好的性能。本文结合了 DNN 和 HMRF 的优点来训练模型,深度学习中的标注量不是那么大,从而得到了一种更有效的脑血管分割方法。
更新日期:2020-01-10
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