For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a bimodal fusion of amplitude and phase congruency data proposed by us in previous work, as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed modification is shown to yield improved results on the standard network over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using amplitude data and intensity invariant phase congruency maps as a data source.

中文翻译：

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使用几何约束深层神经网络的双峰自动颈动脉超声分割。

对于患有颈动脉狭窄的无症状患者，斑块形态的评估是一项重要的临床任务，可以监测斑块破裂的风险和未来中风的发生。超声成像为此提供了一种安全且非侵入性的方式，而中膜外膜边界和颈动脉内膜-内膜边界的分割是该监测过程的重要组成部分。在本文中，我们提出了一种新颖的深层神经网络作为一种全自动分割工具，并将其应用于描绘中膜-外膜边界和管腔-内膜边界。我们开发了一个新的几何约束目标函数，作为网络随机梯度下降优化的一部分，从而针对当前问题进行了调整。此外，我们还将先前工作中提出的幅度和相位一致性数据的双峰融合作为网络的输入，因为后者为网络提供了强度不变的数据源。我们最后报告了在颈动脉横切面上网络的分割性能。对81,000张图像的增强数据集进行了测试，并通过报告DICE的相似性系数，修正的Hausdorff距离，灵敏度和特异性将结果与其他研究进行了比较。我们建议的修改方法在此较大的数据集上显示出在标准网络上产生的改进结果，并且具有完全自动化的优点。我们得出的结论是，深度神经网络提供了一种可靠的训练方式，可以自动分割颈动脉超声图像，