Atrial scar quantification via multi-scale CNN in the graph-cuts framework
Atrial scar quantification via multi-scale CNN in the graph-cuts framework
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DOI：
10.1016/j.media.2019.101595
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发表时间：
2020-02-01
影响因子：
10.9
通讯作者：
Zhuang, Xiahai
中科院分区：
文献类型：
--
作者：
Li, Lei;Wu, Fuping;Zhuang, Xiahai
晚期Gd增强磁共振成像(LGE MRI)似乎是评估房颤(AF)患者瘢痕的一种有前途的替代方法。由于图像质量较低，自动量化和分析心房瘢痕可能具有挑战性。在这项工作中，我们提出了一种基于图割框架的全自动方法，其中图形的势是使用多尺度卷积神经网络(MS-CNN)在左心房(LA)的表面网格上学习的。为了验证，我们纳入了58张手工勾画的图像。MS-CNN能够有效地融合图像的局部和全局纹理信息，明显提高了基于图割的分割精度。当图的t链接权重和n链接权重之间的贡献达到平衡时，分割可以进一步改进。对于LA SCAR的量化，该方法的平均准确率为0.856+/-0.033，Dice评分为0.702+/-0.071。与传统的基于人工勾画LA进行初始化的方法相比，我们的方法是全自动的，并且表现出明显更好的Dice分数和准确性(p<；0.01)。该方法对房颤的诊断和预后有潜在的应用价值。(C)2019年提交人(S)。爱思唯尔出版公司(Elsevier B.V.)
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 +/- 0.033 and mean Dice score of 0.702 +/- 0.071 for LA scar quantification. Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be potentially useful in diagnosis and prognosis of AF. (C) 2019 The Author(s). Published by Elsevier B.V.